IELTS Reading: AI Dự Đoán Thị Trường Tài Chính – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Trí tuệ nhân tạo (AI) trong dự đoán thị trường tài chính là một chủ đề nóng hổi và ngày càng xuất hiện phổ biến trong các kỳ thi IELTS Reading gần đây. Với sự phát triển vượt bậc của công nghệ, việc ứng dụng AI vào lĩnh vực tài chính đã trở thành xu hướng toàn cầu, khiến chủ đề này trở nên vô cùng thiết thực và hấp dẫn đối với các nhà ra đề IELTS.

Trong bài viết này, bạn sẽ được trải nghiệm một đề thi IELTS Reading hoàn chỉnh với ba passages có độ khó tăng dần từ Easy đến Hard, bao gồm 40 câu hỏi đa dạng giống như thi thật. Đề thi được thiết kế dựa trên cấu trúc chuẩn của Cambridge IELTS, giúp bạn làm quen với nhiều dạng câu hỏi khác nhau như Multiple Choice, True/False/Not Given, Matching Headings, và Summary Completion.

Bên cạnh bài thi, bạn sẽ nhận được đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin trong bài, cách paraphrase từ khóa, và chiến lược làm bài hiệu quả. Phần từ vựng quan trọng được trình bày dưới dạng bảng với phiên âm, nghĩa tiếng Việt và ví dụ sử dụng sẽ giúp bạn mở rộng vốn từ học thuật.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, đặc biệt hữu ích cho những ai đang nhắm đến band điểm 7.0-8.0 trong phần Reading.

Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test kéo dài 60 phút và bao gồm 3 passages với tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được tính 1 điểm, không có điểm âm khi trả lời sai.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (độ khó Easy)
• Passage 2: 18-20 phút (độ khó Medium)
• Passage 3: 23-25 phút (độ khó Hard)

Lưu ý dành 2-3 phút cuối để chuyển đáp án vào Answer Sheet, vì bạn sẽ không có thêm thời gian sau khi hết 60 phút.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:

1. Multiple Choice – Chọn đáp án đúng từ A, B, C hoặc D
2. True/False/Not Given – Xác định thông tin đúng, sai hay không được đề cập
3. Matching Information – Nối thông tin với đoạn văn tương ứng
4. Matching Headings – Chọn tiêu đề phù hợp cho mỗi đoạn
5. Sentence Completion – Hoàn thành câu với từ trong bài
6. Summary Completion – Điền từ vào tóm tắt đoạn văn
7. Short-answer Questions – Trả lời câu hỏi ngắn (không quá 3 từ)

IELTS Reading Practice Test

PASSAGE 1 – The Dawn of AI in Financial Forecasting

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The financial markets have always been characterized by volatility and unpredictability, making accurate forecasting a challenge that has captivated economists and traders for centuries. In recent years, however, artificial intelligence (AI) has emerged as a revolutionary tool that is transforming how we predict market movements and make investment decisions. Unlike traditional methods that rely heavily on human analysis and historical patterns, AI systems can process vast amounts of data at incredible speeds, identifying patterns that would be impossible for humans to detect.

The journey of AI in financial markets began in the 1980s when the first algorithmic trading systems were introduced on Wall Street. These early systems were relatively simple, using basic mathematical models to execute trades automatically based on predefined rules. However, they laid the groundwork for what would become a multi-billion dollar industry. Today’s AI-powered trading platforms are exponentially more sophisticated, utilizing machine learning algorithms that can adapt and improve their predictions over time without explicit programming.

One of the most significant advantages of AI In Predicting Financial Markets is its ability to analyze multiple data sources simultaneously. Traditional analysts might focus on company financial reports, economic indicators, and historical price movements. In contrast, modern AI systems can incorporate social media sentiment, news articles, weather patterns, geopolitical events, and even satellite imagery to predict market trends. For example, AI algorithms can analyze millions of tweets to gauge public sentiment about a particular stock or scan satellite images of retail parking lots to estimate a company’s sales before official reports are released.

Machine learning, a subset of AI, has proven particularly effective in financial forecasting. These systems use neural networks modeled after the human brain to recognize complex patterns in data. When trained on years of historical market data, machine learning models can identify subtle correlations and anomalies that might indicate future price movements. Some hedge funds have reported that their AI-powered trading strategies consistently outperform traditional human-managed portfolios, although critics argue that past performance does not guarantee future results.

Despite its impressive capabilities, AI in financial prediction is not without limitations. One major challenge is the “black box” problem – the difficulty in understanding exactly how an AI system arrives at its predictions. This lack of transparency can be problematic in the highly regulated financial industry, where decisions must often be explained and justified to regulators and clients. Additionally, AI systems are only as good as the data they are trained on. If the training data contains biases or does not account for unprecedented events like the 2008 financial crisis or the COVID-19 pandemic, the AI’s predictions may be inaccurate or misleading.

Another concern is the potential for systemic risk if too many market participants rely on similar AI systems. If multiple AI algorithms identify the same trading opportunity simultaneously, they might execute trades in the same direction, creating artificial market movements or exacerbating volatility. This phenomenon, known as “flash crashes”, has already occurred several times in major stock markets, causing prices to plunge and recover within minutes due to algorithmic trading.

The regulatory landscape surrounding AI in finance is still evolving. Financial authorities worldwide are grappling with how to oversee these technologies effectively while not stifling innovation. Some countries have introduced requirements for algorithmic accountability, mandating that financial institutions must be able to explain the logic behind their AI-driven decisions. Others are developing “sandboxes” – controlled environments where fintech companies can test new AI applications under regulatory supervision.

Looking ahead, experts predict that AI will become even more integral to financial markets. The next generation of AI systems may incorporate quantum computing, which could solve complex financial calculations millions of times faster than today’s computers. There is also growing interest in explainable AI – systems designed to provide clear reasoning for their predictions, addressing the transparency concerns that currently limit AI adoption in some areas of finance.

For individual investors, the proliferation of AI in finance presents both opportunities and challenges. While AI-powered robo-advisors have made sophisticated investment strategies accessible to ordinary people at low cost, there is also a risk of over-reliance on technology. Financial experts emphasize that AI should be viewed as a tool to augment human decision-making rather than replace it entirely. The most successful investment approaches will likely combine AI’s data-processing capabilities with human judgment, ethical considerations, and understanding of broader economic contexts that machines may not fully comprehend.

Questions 1-13

Questions 1-5: Multiple Choice

Choose the correct letter, A, B, C, or D.

1. According to the passage, what distinguishes AI systems from traditional forecasting methods?
   A) They are more expensive to implement
   B) They can analyze data much faster than humans
   C) They rely more on historical patterns
   D) They require less maintenance

2. The first algorithmic trading systems in the 1980s were:
   A) highly complex and difficult to understand
   B) based on simple mathematical rules
   C) immediately successful on a large scale
   D) rejected by most financial institutions

3. Which of the following is NOT mentioned as a data source used by modern AI systems?
   A) Social media posts
   B) Satellite imagery
   C) DNA sequences
   D) Weather patterns

4. The “black box” problem refers to:
   A) the high cost of AI systems
   B) the difficulty in understanding AI’s decision-making process
   C) the physical appearance of computer servers
   D) illegal trading activities

5. What do experts predict about the future of AI in finance?
   A) It will be completely replaced by human analysts
   B) It will become less important over time
   C) It may incorporate quantum computing technology
   D) It will only be used by large institutions

Questions 6-9: True/False/Not Given

Do the following statements agree with the information given in the passage? Write:

TRUE if the statement agrees with the information
FALSE if the statement contradicts the information
NOT GIVEN if there is no information on this

6. Hedge funds using AI strategies always perform better than human-managed portfolios.

7. AI systems can analyze satellite images to predict company sales figures.

8. The 2008 financial crisis was accurately predicted by AI systems.

9. Some countries require financial institutions to explain their AI-driven decisions.

Questions 10-13: Sentence Completion

Complete the sentences below. Choose NO MORE THAN TWO WORDS from the passage for each answer.

10. AI systems use __ that are modeled after the structure of the human brain.

11. When many AI algorithms execute trades simultaneously, they can create __ in the market.

12. Financial authorities are developing controlled environments called __ to test new AI applications.

13. For ordinary investors, __ have made advanced investment strategies more affordable and accessible.

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PASSAGE 2 – Machine Learning Algorithms: The Engine Behind Financial Predictions

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The proliferation of machine learning in financial market prediction represents a paradigm shift in how investment decisions are formulated and executed. At the heart of this transformation lie sophisticated algorithms that have evolved considerably from their rudimentary predecessors. Understanding these algorithms and their applications provides crucial insight into the mechanics of modern financial forecasting and the implications for market dynamics.

Supervised learning algorithms form the foundation of most predictive models in finance. These systems are trained on labeled datasets where historical inputs are paired with known outcomes. For instance, an algorithm might learn from decades of data showing how various economic indicators correlated with subsequent stock price movements. The most commonly employed supervised learning techniques include regression analysis, decision trees, and support vector machines. Regression models, particularly linear regression and its more complex variant polynomial regression, excel at identifying relationships between variables and predicting continuous outcomes such as stock prices or currency exchange rates.

However, the financial markets’ inherent non-linearity often limits the effectiveness of simple regression models. This is where more advanced techniques like random forests and gradient boosting machines come into play. Random forests construct multiple decision trees during training and output the average prediction, thereby reducing overfitting – a common problem where models perform well on training data but poorly on new, unseen data. Gradient boosting, conversely, builds trees sequentially, with each new tree correcting errors made by previous ones. These ensemble methods have demonstrated remarkable accuracy in predicting market trends, particularly in volatile conditions where traditional models falter.

Deep learning, a subset of machine learning inspired by the structure of the human brain, has generated considerable excitement in the financial sector. Convolutional Neural Networks (CNNs), originally developed for image recognition, are now being applied to identify patterns in financial charts and time-series data. Recurrent Neural Networks (RNNs) and their more sophisticated variant, Long Short-Term Memory (LSTM) networks, are particularly well-suited for sequential data analysis. These architectures can remember information over extended periods, making them ideal for capturing the temporal dependencies inherent in financial data. Some investment firms have reported that LSTM networks can predict short-term price movements with accuracy rates exceeding 60%, a significant improvement over random chance in efficient markets.

The feature engineering process – selecting and transforming input variables – critically influences algorithm performance. In financial applications, features might include technical indicators like moving averages and momentum oscillators, fundamental metrics such as price-to-earnings ratios, or alternative data like consumer sentiment indices. Recent advances in automated feature engineering have enabled algorithms to discover relevant features autonomously, reducing human bias and potentially uncovering non-obvious predictive signals.

Natural Language Processing (NLP), another branch of AI, has opened new frontiers in financial prediction. NLP algorithms can analyze unstructured textual data from earnings reports, news articles, and social media to gauge market sentiment and predict price movements. Sentiment analysis algorithms assign scores to text based on emotional tone, while more advanced techniques like named entity recognition can identify specific companies, people, or events mentioned in documents. Studies have shown that incorporating NLP-derived sentiment scores into trading algorithms can enhance returns, particularly around major news events or earnings announcements.

Despite these technological advances, several methodological challenges persist. Selection bias occurs when training data is not representative of future market conditions, leading to models that perform poorly in real-world applications. The look-ahead bias, where information not available at the time of prediction is inadvertently included in the model, can produce artificially inflated performance metrics during testing. Additionally, financial markets exhibit non-stationarity – their statistical properties change over time – which means models require continuous retraining and recalibration to maintain effectiveness.

The Efficient Market Hypothesis (EMH), which posits that asset prices reflect all available information, poses a theoretical challenge to AI-based prediction. If markets are truly efficient, consistent outperformance should be impossible. However, proponents of AI in finance argue that machine learning can identify market inefficiencies and behavioral biases that create predictable patterns. The debate between efficient market theorists and AI practitioners remains contentious, with evidence supporting both positions depending on the market, timeframe, and asset class examined.

Renewable energy innovations in developing countries face similar challenges in terms of data availability and predictability, though the context differs significantly from financial markets.

Regulatory considerations also shape how machine learning is deployed in finance. The European Union’s General Data Protection Regulation (GDPR) includes a “right to explanation” for automated decisions affecting individuals, which has implications for AI-driven credit scoring and loan approval systems. In the United States, the Securities and Exchange Commission (SEC) has increased scrutiny of algorithmic trading practices, particularly following several high-profile flash crashes attributed to malfunctioning algorithms. These regulatory pressures are driving development of interpretable models that balance predictive power with transparency.

The integration of alternative data sources represents the frontier of machine learning in finance. Satellite imagery tracking retail foot traffic, credit card transaction data, and even job posting analytics are being incorporated into predictive models. These unconventional datasets can provide leading indicators of company performance before traditional financial metrics reflect changes. However, accessing and processing alternative data raises ethical questions about privacy and the potential for information asymmetry between large institutions with substantial resources and smaller market participants.

Looking forward, the convergence of quantum computing and machine learning may revolutionize financial prediction. Quantum algorithms could theoretically solve optimization problems that are currently intractable, enabling more sophisticated portfolio allocation strategies and risk management techniques. However, practical quantum computing applications remain years away, and their ultimate impact on financial markets is still speculative.

Questions 14-26

Questions 14-18: Matching Headings

The passage has eleven paragraphs. Choose the correct heading for paragraphs B-F from the list of headings below.

List of Headings:
i. The role of textual data in market prediction
ii. Theoretical objections to predictive algorithms
iii. Training methods for financial AI systems
iv. Advanced tree-based prediction techniques
v. Brain-inspired algorithms for sequential data
vi. Privacy laws affecting AI deployment
vii. The importance of selecting input variables
viii. Future technological breakthroughs
ix. Problems with training data quality

14. Paragraph B
15. Paragraph C
16. Paragraph D
17. Paragraph E
18. Paragraph F

Questions 19-23: Yes/No/Not Given

Do the following statements agree with the claims of the writer? Write:

YES if the statement agrees with the claims of the writer
NO if the statement contradicts the claims of the writer
NOT GIVEN if it is impossible to say what the writer thinks about this

19. Random forests are more effective than gradient boosting in all market conditions.

20. LSTM networks can remember information over long periods, making them suitable for financial data analysis.

21. The Efficient Market Hypothesis suggests that AI-based prediction should not work consistently.

22. Quantum computing is currently being used by major investment firms.

23. Alternative data sources provide advantages mainly to smaller investment firms.

Questions 24-26: Summary Completion

Complete the summary below. Choose NO MORE THAN TWO WORDS from the passage for each answer.

Natural Language Processing enables algorithms to analyze text from various sources to determine market sentiment. Advanced techniques such as (24) __ can identify specific entities mentioned in documents. However, several challenges affect algorithm accuracy, including (25) __, which occurs when training data doesn’t represent future conditions, and (26) __, where markets’ statistical properties change over time.

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PASSAGE 3 – The Epistemological and Systemic Implications of AI-Driven Market Prediction

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The ascendancy of artificial intelligence in financial market prediction transcends mere technological innovation; it represents a fundamental reconceptualization of how knowledge is generated, validated, and operationalized within economic systems. This transformation raises profound questions about the nature of market efficiency, the epistemological validity of algorithm-derived insights, and the potential for emergent systemic risks that could fundamentally alter the architecture of global financial markets. As AI systems become increasingly autonomous and their decision-making processes more opaque, we must critically examine not only their predictive capabilities but also their broader implications for market stability, wealth distribution, and the very ontology of financial value.

The traditional epistemological framework governing financial analysis rests on the assumption that human cognition, albeit bounded by computational limitations, provides the most reliable mechanism for interpreting market signals and making investment decisions. This anthropocentric view has been progressively challenged by AI systems that demonstrate superior pattern recognition capabilities across multiple dimensions simultaneously. However, this superiority comes with a significant epistemological paradox: while AI algorithms may generate more accurate predictions, the inscrutability of their internal processes undermines our ability to understand why certain predictions are made. This creates what philosophers call a “knowledge-action gap” – we can act on AI recommendations without truly comprehending the underlying rationale.

The concept of “algorithmic objectivity” – the notion that AI systems make unbiased, purely data-driven decisions – has been thoroughly debunked by recent research. AI algorithms inevitably encode the biases present in their training data, the normative assumptions of their designers, and the socioeconomic context in which they operate. In financial markets, this can manifest in self-reinforcing feedback loops where algorithmic predictions influence market behavior, which in turn generates data that confirms the original predictions. This phenomenon, termed “performativity” in economic sociology, suggests that AI systems do not merely predict markets but actively construct them through their interventions.

The reflexivity inherent in AI-driven markets presents unprecedented challenges for regulatory frameworks designed for human-mediated trading. When algorithms respond to other algorithms in microsecond timeframes, traditional circuit breakers and regulatory interventions become ineffective. The 2010 “Flash Crash,” during which the Dow Jones Industrial Average plunged nearly 1,000 points in minutes before recovering, exemplifies how algorithmic interactions can generate cascading instabilities. Subsequent analysis revealed that the crash resulted from a complex interplay between multiple algorithmic trading systems, each responding rationally to immediate market conditions but collectively producing an irrational outcome.

How is the rise of electric vehicles affecting the oil industry? demonstrates similar patterns of technological disruption and market adaptation, though with longer timeframes and more transparent causal mechanisms.

From a game-theoretic perspective, the proliferation of AI in financial markets creates a peculiar adversarial landscape. Unlike traditional market competition where participants operate with comparable cognitive capabilities and information processing speeds, AI-dominated markets feature extreme heterogeneity in computational power and algorithmic sophistication. This asymmetry potentially violates the common knowledge assumptions underlying most economic models, where all participants are assumed to have similar reasoning capabilities. When some market actors possess AI systems capable of hyper-dimensional analysis unavailable to others, the very notion of “fair” market competition becomes problematic.

The temporal dynamics of AI prediction introduce another layer of complexity. Most machine learning algorithms are optimized for short-term forecasting, as prediction accuracy decreases exponentially with time horizons. This creates a systematic bias toward short-term trading strategies, potentially exacerbating market volatility and undermining the price discovery function that should reflect long-term fundamental values. Some economists argue this represents a form of “temporal market failure” where the aggregate effect of individually rational AI-driven decisions produces suboptimal social outcomes, particularly for long-term capital allocation toward productive investments rather than speculative activities.

The concentration of AI capabilities among a relatively small number of large financial institutions raises concerns about systemic risk and market structure. The oligopolistic nature of advanced AI development – requiring substantial computational resources, proprietary datasets, and specialized expertise – means that market-moving AI systems are predominantly controlled by a handful of major players. This concentration creates “too big to fail” scenarios not merely at the institutional level but at the algorithmic level, where the malfunction or unintended behavior of a widely-deployed AI system could trigger systemic contagion.

Ethical dimensions of AI-driven financial prediction extend beyond traditional concerns about market manipulation and insider trading. The use of alternative data sources – including social media activity, geolocation data, and consumer behavior patterns – raises privacy concerns and questions about informational justice. When AI algorithms extract predictive signals from individuals’ digital footprints without explicit consent or compensation, a value transfer occurs from data subjects to algorithm operators. This asymmetry challenges conventional notions of property rights in information and raises questions about whether individuals should receive economic rent for data that generates alpha (excess returns above market benchmarks).

The philosophical implications of superior AI prediction capabilities touch on fundamental questions about determinism and free will in economic systems. If sufficiently advanced AI can predict market movements with high accuracy, it suggests that markets may be more deterministic than previously believed, governed by identifiable patterns rather than fundamental uncertainty. This challenges the ontological status of randomness in financial theory and raises questions about whether markets retain their function as information aggregation mechanisms or become increasingly self-referential systems responding primarily to algorithmic signals rather than underlying economic realities.

How global warming is changing agricultural practices illustrates another domain where predictive capabilities must grapple with complex, adaptive systems exhibiting both deterministic patterns and irreducible uncertainty.

Looking toward future developments, the emergence of artificial general intelligence (AGI) – AI systems with human-like general reasoning capabilities – could fundamentally transform financial markets. AGI systems might develop novel trading strategies beyond human comprehension, identify causal relationships that elude current analytical methods, or even recognize fundamental limitations in current financial architectures that suggest alternative organizational forms. However, the transition to AGI-dominated markets would likely involve turbulent disruption, as existing market structures, regulatory frameworks, and wealth distribution patterns would require fundamental recalibration.

The governance challenge posed by autonomous AI in finance extends beyond regulation to questions of algorithmic accountability and liability attribution. When AI systems make decisions that result in substantial losses or market disruptions, establishing legal responsibility becomes problematic. Should liability rest with the algorithm’s designers, the financial institutions deploying it, the executives overseeing its use, or should AI systems themselves possess some form of legal personhood? These questions challenge traditional jurisprudential frameworks and may require entirely new legal doctrines adapted to the realities of autonomous algorithmic agents.

The cultural and psychological impacts of AI-driven markets deserve consideration beyond purely technical and economic dimensions. As human traders and analysts find themselves increasingly marginalized by superior algorithmic performance, questions arise about the future of financial expertise as a professional identity and the social meaning of market participation. The democratization of investment through AI-powered robo-advisors paradoxically occurs alongside the mystification of investment decision-making, as retail investors employ tools whose operation they cannot fully understand. This tension between accessibility and comprehension may reshape how societies understand and engage with financial markets.

Questions 27-40

Questions 27-31: Multiple Choice

Choose the correct letter, A, B, C, or D.

27. According to the passage, the “knowledge-action gap” refers to:
    A) The difference between theoretical knowledge and practical skills
    B) Acting on AI recommendations without understanding the reasoning
    C) The time delay between acquiring knowledge and using it
    D) Different knowledge levels among market participants

28. The term “performativity” in economic sociology means that AI systems:
    A) Function more efficiently than human traders
    B) Only work well in certain market conditions
    C) Actively shape markets through their predictions
    D) Require regular performance evaluations

29. The 2010 Flash Crash demonstrated that:
    A) Human traders make better decisions than algorithms
    B) Multiple algorithms can collectively produce irrational outcomes
    C) Circuit breakers are effective in preventing market crashes
    D) Traditional regulatory methods work well for algorithmic trading

30. The passage suggests that AI dominance in markets creates an unfair environment because:
    A) AI systems are too expensive for small investors
    B) Different participants have vastly different computational capabilities
    C) AI algorithms always produce higher returns
    D) Human traders refuse to adapt to new technologies

31. What does the author mean by “temporal market failure”?
    A) AI systems occasionally stop functioning
    B) Markets only operate during certain hours
    C) Short-term focus undermines long-term value discovery
    D) Time zones create trading disadvantages

Questions 32-36: Matching Features

Match each concern (Questions 32-36) with the correct aspect of AI in finance (A-H).

Concerns:
32. Legal responsibility for algorithmic decisions is unclear
33. Few institutions control advanced AI capabilities
34. Individuals’ data generates profits without their consent
35. Markets may become self-referential rather than reflect economic reality
36. Professional identity of human traders is threatened

Aspects:
A) Epistemological implications
B) Systemic risk
C) Ethical dimensions
D) Governance challenges
E) Temporal dynamics
F) Cultural impacts
G) Concentration of capabilities
H) Philosophical implications

Questions 37-40: Short-answer Questions

Answer the questions below. Choose NO MORE THAN THREE WORDS from the passage for each answer.

37. What type of intelligence could develop trading strategies beyond human understanding?

38. What concept describes the decrease in AI prediction accuracy over longer timeframes?

39. What do some economists call the situation where individually rational AI decisions produce poor social outcomes?

40. What term describes returns that exceed standard market benchmarks?

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Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. B
3. C
4. B
5. C
6. FALSE
7. TRUE
8. NOT GIVEN
9. TRUE
10. neural networks
11. artificial market movements / flash crashes
12. sandboxes
13. robo-advisors

PASSAGE 2: Questions 14-26

14. iii
15. iv
16. v
17. vii
18. i
19. NOT GIVEN
20. YES
21. YES
22. NOT GIVEN
23. NO
24. named entity recognition
25. selection bias
26. non-stationarity

PASSAGE 3: Questions 27-40

27. B
28. C
29. B
30. B
31. C
32. D
33. G
34. C
35. H
36. F
37. artificial general intelligence / AGI
38. time horizons
39. temporal market failure
40. alpha

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Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: distinguishes AI systems, traditional forecasting methods
• Vị trí trong bài: Đoạn 1, dòng 4-6
• Giải thích: Câu “Unlike traditional methods that rely heavily on human analysis and historical patterns, AI systems can process vast amounts of data at incredible speeds” cho thấy sự khác biệt chính là tốc độ xử lý dữ liệu. Đáp án B “can analyze data much faster than humans” là paraphrase chính xác của ý này.

Câu 2: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: first algorithmic trading systems, 1980s
• Vị trí trong bài: Đoạn 2, dòng 2-4
• Giải thích: “These early systems were relatively simple, using basic mathematical models to execute trades automatically based on predefined rules” khẳng định rằng các hệ thống đầu tiên dựa trên các quy tắc toán học đơn giản.

Câu 6: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: hedge funds, AI strategies, always perform better
• Vị trí trong bài: Đoạn 4, dòng cuối
• Giải thích: Bài viết nói “Some hedge funds have reported that their AI-powered trading strategies consistently outperform traditional human-managed portfolios, although critics argue that past performance does not guarantee future results.” Từ “although critics argue” và việc chỉ nói “some hedge funds” cho thấy không phải luôn luôn tốt hơn, do đó đáp án là FALSE.

Câu 7: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: satellite images, predict company sales
• Vị trí trong bài: Đoạn 3, dòng 7-9
• Giải thích: “AI algorithms can analyze millions of tweets to gauge public sentiment about a particular stock or scan satellite images of retail parking lots to estimate a company’s sales before official reports are released” xác nhận rõ ràng thông tin này là đúng.

Câu 10: neural networks

• Dạng câu hỏi: Sentence Completion
• Từ khóa: modeled after, human brain
• Vị trí trong bài: Đoạn 4, dòng 3-4
• Giải thích: “These systems use neural networks modeled after the human brain to recognize complex patterns in data” chứa đáp án chính xác.

Câu 13: robo-advisors

• Dạng câu hỏi: Sentence Completion
• Từ khóa: ordinary investors, advanced investment strategies, affordable
• Vị trí trong bài: Đoạn 9, dòng 2-3
• Giải thích: “While AI-powered robo-advisors have made sophisticated investment strategies accessible to ordinary people at low cost” cho thấy robo-advisors là đáp án phù hợp.

Passage 2 – Giải Thích

Câu 14: iii (Training methods for financial AI systems)

• Dạng câu hỏi: Matching Headings
• Vị trí trong bài: Paragraph B (đoạn 2)
• Giải thích: Đoạn này mô tả “Supervised learning algorithms” và cách chúng được “trained on labeled datasets where historical inputs are paired with known outcomes.” Đây rõ ràng là về phương pháp huấn luyện AI.

Câu 15: iv (Advanced tree-based prediction techniques)

• Dạng câu hỏi: Matching Headings
• Vị trí trong bài: Paragraph C (đoạn 3)
• Giải thích: Đoạn này tập trung vào “random forests” và “gradient boosting machines” – cả hai đều là kỹ thuật dựa trên cây quyết định (tree-based). Từ “advanced” phù hợp với việc mô tả chúng phức tạp hơn regression đơn giản.

Câu 20: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: LSTM networks, remember information, long periods
• Vị trí trong bài: Đoạn 4, giữa đoạn
• Giải thích: “These architectures can remember information over extended periods, making them ideal for capturing the temporal dependencies inherent in financial data” khớp hoàn toàn với phát biểu.

Câu 21: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Efficient Market Hypothesis, AI prediction, not work consistently
• Vị trí trong bài: Đoạn 8, đầu đoạn
• Giải thích: “The Efficient Market Hypothesis (EMH), which posits that asset prices reflect all available information, poses a theoretical challenge to AI-based prediction. If markets are truly efficient, consistent outperformance should be impossible.” Điều này xác nhận rằng theo EMH, AI không thể hoạt động hiệu quả một cách nhất quán.

Câu 24: named entity recognition

• Dạng câu hỏi: Summary Completion
• Từ khóa: advanced techniques, identify specific entities
• Vị trí trong bài: Đoạn 6, giữa đoạn
• Giải thích: “more advanced techniques like named entity recognition can identify specific companies, people, or events mentioned in documents” cho đáp án chính xác.

Câu 26: non-stationarity

• Dạng câu hỏi: Summary Completion
• Từ khóa: markets’ statistical properties change
• Vị trí trong bài: Đoạn 7, cuối đoạn
• Giải thích: “financial markets exhibit non-stationarity – their statistical properties change over time” khớp với mô tả trong câu hỏi.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: knowledge-action gap
• Vị trí trong bài: Đoạn 2, cuối đoạn
• Giải thích: “This creates what philosophers call a ‘knowledge-action gap’ – we can act on AI recommendations without truly comprehending the underlying rationale” mô tả chính xác khái niệm này là hành động dựa trên khuyến nghị AI mà không hiểu lý do.

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: performativity, economic sociology
• Vị trí trong bài: Đoạn 3, cuối đoạn
• Giải thích: “This phenomenon, termed ‘performativity’ in economic sociology, suggests that AI systems do not merely predict markets but actively construct them through their interventions” cho thấy AI tích cực định hình thị trường thông qua dự đoán của chúng.

Câu 29: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: 2010 Flash Crash, demonstrated
• Vị trí trong bài: Đoạn 4, giữa và cuối đoạn
• Giải thích: “Subsequent analysis revealed that the crash resulted from a complex interplay between multiple algorithmic trading systems, each responding rationally to immediate market conditions but collectively producing an irrational outcome” cho thấy nhiều thuật toán cùng tạo ra kết quả phi lý.

Câu 32: D (Governance challenges)

• Dạng câu hỏi: Matching Features
• Từ khóa: legal responsibility, algorithmic decisions, unclear
• Vị trí trong bài: Đoạn 11, đầu đoạn
• Giải thích: “The governance challenge posed by autonomous AI in finance extends beyond regulation to questions of algorithmic accountability and liability attribution. When AI systems make decisions that result in substantial losses or market disruptions, establishing legal responsibility becomes problematic.”

Câu 33: G (Concentration of capabilities)

• Dạng câu hỏi: Matching Features
• Từ khóa: few institutions, control, advanced AI
• Vị trí trong bài: Đoạn 8, đầu đoạn
• Giải thích: “The concentration of AI capabilities among a relatively small number of large financial institutions raises concerns about systemic risk and market structure.”

Câu 37: artificial general intelligence / AGI

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: develop trading strategies, beyond human understanding
• Vị trí trong bài: Đoạn 10, đầu đoạn
• Giải thích: “AGI systems might develop novel trading strategies beyond human comprehension” cung cấp đáp án trực tiếp.

Câu 40: alpha

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: returns, exceed, market benchmarks
• Vị trí trong bài: Đoạn 9, cuối đoạn
• Giải thích: “individuals should receive economic rent for data that generates alpha (excess returns above market benchmarks)” định nghĩa rõ ràng alpha là lợi nhuận vượt chuẩn thị trường.

Hình minh họa về AI dự đoán thị trường tài chính trong đề thi IELTS Reading với các biểu đồ và thuật toán

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Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
volatility	n	/ˌvɒləˈtɪləti/	Tính biến động, dao động	financial markets have always been characterized by volatility	market volatility, price volatility
unpredictability	n	/ˌʌnprɪˌdɪktəˈbɪləti/	Tính không thể dự đoán	volatility and unpredictability	inherent unpredictability
revolutionary	adj	/ˌrevəˈluːʃənəri/	Mang tính cách mạng	AI has emerged as a revolutionary tool	revolutionary technology, revolutionary approach
algorithmic trading	n phrase	/ˌælɡəˈrɪðmɪk ˈtreɪdɪŋ/	Giao dịch thuật toán	the first algorithmic trading systems were introduced	algorithmic trading systems, high-frequency trading
exponentially	adv	/ˌekspəˈnenʃəli/	Theo cấp số mũ, rất nhanh	exponentially more sophisticated	grow exponentially, increase exponentially
geopolitical	adj	/ˌdʒiːəʊpəˈlɪtɪkl/	Thuộc về địa chính trị	geopolitical events	geopolitical tensions, geopolitical risks
neural networks	n phrase	/ˈnjʊərəl ˈnetwɜːks/	Mạng nơ-ron nhân tạo	neural networks modeled after the human brain	artificial neural networks, deep neural networks
correlations	n	/ˌkɒrəˈleɪʃənz/	Mối tương quan	identify subtle correlations	strong correlation, positive correlation
hedge funds	n phrase	/hedʒ fʌndz/	Quỹ đầu cơ	Some hedge funds have reported	hedge fund managers, alternative investments
transparency	n	/trænsˈpærənsi/	Tính minh bạch	lack of transparency	financial transparency, corporate transparency
systemic risk	n phrase	/sɪˈstemɪk rɪsk/	Rủi ro hệ thống	potential for systemic risk	systemic financial risk, reduce systemic risk
flash crashes	n phrase	/flæʃ ˈkræʃɪz/	Sự sụp đổ chớp nhoáng (thị trường)	This phenomenon, known as flash crashes	prevent flash crashes, algorithmic flash crashes

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
proliferation	n	/prəˌlɪfəˈreɪʃən/	Sự gia tăng, phát triển nhanh	The proliferation of machine learning	nuclear proliferation, rapid proliferation
paradigm shift	n phrase	/ˈpærədaɪm ʃɪft/	Sự thay đổi mô hình tư duy	represents a paradigm shift	major paradigm shift, technological paradigm
supervised learning	n phrase	/ˈsuːpəvaɪzd ˈlɜːnɪŋ/	Học có giám sát	Supervised learning algorithms form the foundation	supervised learning techniques, machine learning
overfitting	n	/ˌəʊvəˈfɪtɪŋ/	Hiện tượng quá khớp (mô hình)	reducing overfitting	avoid overfitting, prevent overfitting
ensemble methods	n phrase	/ɒnˈsɒmbl ˈmeθədz/	Phương pháp tổ hợp	These ensemble methods have demonstrated	ensemble learning, ensemble techniques
temporal dependencies	n phrase	/ˈtempərəl dɪˈpendənsiz/	Sự phụ thuộc theo thời gian	capturing the temporal dependencies	time-series analysis, sequential patterns
feature engineering	n phrase	/ˈfiːtʃə ˌendʒɪˈnɪərɪŋ/	Kỹ thuật thiết kế đặc trưng	The feature engineering process	automated feature engineering, feature selection
sentiment analysis	n phrase	/ˈsentɪmənt əˈnæləsɪs/	Phân tích cảm xúc/xu hướng	Sentiment analysis algorithms assign scores	text sentiment analysis, social media sentiment
selection bias	n phrase	/sɪˈlekʃən ˈbaɪəs/	Thiên lệch chọn mẫu	Selection bias occurs when training data	avoid selection bias, reduce bias
look-ahead bias	n phrase	/lʊk əˈhed ˈbaɪəs/	Thiên lệch nhìn trước	The look-ahead bias, where information not available	prevent look-ahead bias, historical data
non-stationarity	n	/nɒn ˌsteɪʃəˈnærəti/	Tính không dừng (thống kê)	financial markets exhibit non-stationarity	deal with non-stationarity, time-varying properties
Efficient Market Hypothesis	n phrase	/ɪˈfɪʃənt ˈmɑːkɪt haɪˈpɒθəsɪs/	Giả thuyết thị trường hiệu quả	The Efficient Market Hypothesis poses a challenge	EMH theory, market efficiency
interpretable models	n phrase	/ɪnˈtɜːprətəbl ˈmɒdlz/	Các mô hình có thể giải thích	driving development of interpretable models	explainable AI, model transparency
alternative data	n phrase	/ɔːlˈtɜːnətɪv ˈdeɪtə/	Dữ liệu thay thế/phi truyền thống	integration of alternative data sources	alternative data providers, non-traditional data
leading indicators	n phrase	/ˈliːdɪŋ ˈɪndɪkeɪtəz/	Chỉ số dẫn đầu	provide leading indicators of company performance	economic leading indicators, predictive signals

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
ascendancy	n	/əˈsendənsi/	Sự vượt trội, thống trị	The ascendancy of artificial intelligence	gain ascendancy, rise to ascendancy
reconceptualization	n	/ˌriːkənˌseptʃuəlaɪˈzeɪʃən/	Sự tái khái niệm hóa	represents a fundamental reconceptualization	theoretical reconceptualization
epistemological	adj	/ɪˌpɪstəməˈlɒdʒɪkəl/	Thuộc về nhận thức luận	the epistemological validity	epistemological framework, philosophical questions
emergent systemic risks	n phrase	/ɪˈmɜːdʒənt sɪˈstemɪk rɪsks/	Rủi ro hệ thống mới nổi	potential for emergent systemic risks	systemic financial risks, identify emerging risks
ontology	n	/ɒnˈtɒlədʒi/	Bản thể luận	the very ontology of financial value	ontological status, philosophical ontology
inscrutability	n	/ɪnˌskruːtəˈbɪləti/	Tính khó hiểu, bí ẩn	the inscrutability of their internal processes	algorithmic inscrutability, computational opacity
performativity	n	/pəˌfɔːməˈtɪvəti/	Tính hình thành hiện thực	termed performativity in economic sociology	market performativity, self-fulfilling prophecy
reflexivity	n	/ˌriːflekˈsɪvəti/	Tính phản xạ/tự phản chiếu	The reflexivity inherent in AI-driven markets	market reflexivity, reflexive relationship
cascading instabilities	n phrase	/kæsˈkeɪdɪŋ ˌɪnstəˈbɪlətiz/	Sự mất ổn định liên hoàn	generate cascading instabilities	cascade effects, systemic failures
game-theoretic	adj	/ɡeɪm ˌθɪəˈretɪk/	Thuộc về lý thuyết trò chơi	From a game-theoretic perspective	game theory analysis, strategic interactions
adversarial landscape	n phrase	/ˌædvəˈseəriəl ˈlændskeɪp/	Bối cảnh đối kháng	creates a peculiar adversarial landscape	competitive environment, strategic competition
hyper-dimensional	adj	/ˌhaɪpədaɪˈmenʃənl/	Siêu chiều, đa chiều cao	capable of hyper-dimensional analysis	high-dimensional data, complex analysis
oligopolistic	adj	/ˌɒlɪɡəpəˈlɪstɪk/	Thuộc về độc quyền nhóm	The oligopolistic nature of advanced AI	oligopolistic market, concentrated industry
proprietary datasets	n phrase	/prəˈpraɪətəri ˈdeɪtəsets/	Bộ dữ liệu độc quyền	requiring proprietary datasets	proprietary data, exclusive information
informational justice	n phrase	/ˌɪnfəˈmeɪʃənl ˈdʒʌstɪs/	Công bằng thông tin	questions about informational justice	data equity, information rights
determinism	n	/dɪˈtɜːmɪnɪzəm/	Thuyết định mệnh	fundamental questions about determinism	technological determinism, causal determinism
self-referential	adj	/ˌself ˌrefəˈrenʃəl/	Tự tham chiếu	increasingly self-referential systems	self-referential logic, circular reasoning
artificial general intelligence	n phrase	/ˌɑːtɪfɪʃəl ˈdʒenərəl ɪnˈtelɪdʒəns/	Trí tuệ nhân tạo tổng quát	the emergence of AGI	AGI development, strong AI

Học viên luyện tập IELTS Reading với chủ đề AI và tài chính, có sách Cambridge và laptop

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Kết Bài

Chủ đề AI trong dự đoán thị trường tài chính không chỉ phản ánh xu hướng công nghệ hiện đại mà còn thể hiện sự giao thoa giữa khoa học, kinh tế và xã hội – một đặc điểm điển hình của các bài đọc IELTS Reading ở band điểm cao. Việc nắm vững chủ đề này sẽ giúp bạn tự tin hơn khi gặp các passage tương tự về technology, finance, hoặc innovation trong kỳ thi thực tế.

Ba passages trong đề thi mẫu này đã được thiết kế cẩn thận để mô phỏng chính xác cấu trúc và độ khó của bài thi IELTS Reading thật. Passage 1 cung cấp nền tảng kiến thức cơ bản với từ vựng dễ tiếp cận, Passage 2 đi sâu vào các khía cạnh kỹ thuật với terminologies chuyên ngành, và Passage 3 thách thức khả năng phân tích phức tạp với các khái niệm triết học và hệ thống. Sự tăng dần về độ khó này giúp bạn làm quen với áp lực thời gian và yêu cầu tư duy ngày càng cao trong bài thi thực tế.

Phần đáp án chi tiết không chỉ cung cấp đáp án đúng mà còn giải thích cặn kẽ lý do tại sao mỗi đáp án là chính xác, vị trí cụ thể trong bài đọc, và cách nhận biết paraphrase – một kỹ năng then chốt trong IELTS Reading. Việc hiểu rõ logic đằng sau mỗi câu trả lời sẽ giúp bạn phát triển tư duy phản biện và nâng cao khả năng làm bài hiệu quả hơn.

Bảng từ vựng với hơn 40 từ khóa quan trọng được phân loại theo passage sẽ là tài liệu ôn tập quý giá. Hãy chú ý đặc biệt đến các collocations và cách sử dụng từ trong ngữ cảnh, vì đây là những yếu tố quan trọng không chỉ cho phần Reading mà còn cho Writing và Speaking.

Để tận dụng tối đa đề thi này, hãy thực hành trong điều kiện như thi thật: đặt thời gian 60 phút, không tra từ điển, và hoàn thành cả ba passages một lượt. Sau đó, dành thời gian xem lại đáp án, phân tích những câu sai, và học từ vựng mới. Việc lặp lại quá trình này với nhiều đề thi khác nhau sẽ giúp bạn xây dựng sự tự tin và kỹ năng cần thiết để đạt band điểm mục tiêu.

Chúc bạn ôn tập hiệu quả và đạt kết quả cao trong kỳ thi IELTS sắp tới!