# Related Work

## Extreme Volatility in Financial Markets

On August 17, CWM50 held a seminar on ”Global Financial Market Volatility: Challenges and Responses,” Lu Ting, the chief economist of Nomura Securities China, delivered a speech at the conference. Analyzing US macroeconomic data before and after the global financial market volatility shows that the rise in US unemployment does not apply to the Sam principle. On the one hand, there are many distractions to the US unemployment data. On the other hand, macroeconomic data show that the US economy is running smoothly, with no signs of recession. In Ge, Minyue, Zhang, Feng, and Qian, Meng’s paper (2024) [5], the authors explore the integration of artificial intelligence in urban planning and green building technologies, emphasizing its principles, applications, and global case studies. Their work highlights the potential of AI to optimize urban designs and enhance sustainability in building practices, offering a comprehensive overview of its impact on the environment and urban development. Building on their insights, our research applies AI techniques to financial markets, specifically focusing on predicting extreme volatility based on unstructured data. While Ge et al. emphasize AI’s role in sustainable development, we extend the application of AI to the financial sector, using machine learning models to address market instability. This cross-disciplinary approach leverages AI’s versatility, similar to its use in urban planning, to optimize decision-making in a completely different context. Combined with the US economic data analysis and the Bank of Japan’s interest rate hike, the negative impact of the financial market volatility on the economic growth of the US and Japan in the second half of this year should not be overestimated. China should pay more attention to internal economic problems to cope with the downward pressure of the economy in the second half of the year. Regarding coping strategies, it is suggested to formulate policies from three aspects[6-7]:

- Stabilizing the RMB exchange rate.
- Adjusting the stock loan interest rate.
- Boosting domestic demand.

I. Global Financial Market Volatility and the ”Sam Principle” In early August, the volatility of global financial markets was widely discussed. However, over longer timescales, the impact on the worldwide economy is minimal. The US market has gradually recovered, and the Japanese stock market has also recovered. [8]Therefore, after this volatility, with the release of various economic indicators and the adjustment of policy expectations, the global market did not produce panic. There are several reasons for this: First, the unwinding of the yen carry was an isolated event. If the unwinding of arbitrage does not add up to a financial or economic crisis in one country, there will be no global financial turmoil. This experience has been verified in history. Second, the main reasons for triggering large fluctuations in the United States in early August were non-farm and unemployment data. In July, the US non-farm data plunged from 200,000 to 110,000, and the unemployment rate rose to 4.3%, triggering the ”Sam principle” and causing financial markets to weaken. But what has been confirmed in history is not necessarily true today. In Chen, J., Xiao, J., and Xu, W. ’s paper [9], a novel hybrid stacking method is proposed for short-term price forecasting in the electricity trading market. The authors combine various machine learning models to improve the accuracy of predictions, which is crucial for dynamic and high-frequency trading environments like electricity markets. This approach has shown promising results, especially in terms of capturing intricate market behaviors over short periods. Our proposed method extends this work by adapting Chen et al.’s hybrid stacking model to predict extreme volatility in financial markets. While their model primarily targets price forecasting in electricity markets, we adapt it for unstructured data inputs, such as news articles, social media sentiments, and macroeconomic reports, which are increasingly influential in financial markets. Moreover, by leveraging this method, we can capture more complex patterns of market behavior, enhancing the prediction of sudden price swings and volatility spikes that are crucial for risk management and trading strategies. In recent years, the employment problem in the United States has been primarily affected by illegal immigration, supply-side factors after the epidemic, and labour participation rates, so the ”Sam principle” does not necessarily apply[10-12]. The author of the ”Sam Principle,” the Federal Reserve economist Sam himself, also believes that the market has overinterpreted the ”Sam principle.” The trigger for the Sam Principle is likely to be a ”false positive,” with the primary evidence reflected in rising labour participation rates. Interpreting US employment data has become increasingly difficult in recent years. The Sam Principle should not be used to explain the US economy’s current problems. Historically, the Sam Principle worked well in 1990, 2001, and 2008, but the circumstances of those times differ from those of this time. [13]For example, in the US non-farm payrolls data, US household income, and US industrial production data, the average of the four months before the above three economic or financial crises was below 0, but this year, the three data are above 0. 2. The US and Japan are performing smoothly Before and after this turmoil, the data released by the United States reflects the relatively stable operation of the United States economy. [14-17]The PMI for the US services sector came in at 51.4 percent, higher than expected and back above the 50 percent line that separates expansion from contraction. Retail sales figures in the US were also good. The number of Americans filing new claims for unemployment benefits has fallen in the past few weeks. The US economy is experiencing a different recession than Sam’s rule historically suggests. The reasons for this phenomenon are more complex. One reason is the high proportion of public spending in the US. This is markedly different from before the pandemic. The US fiscal deficit is between 5% and 6% of GDP, supporting the US economy. Although the US government has the problem of abusing the sovereign currency, in the short term, the high fiscal deficit has a specific supporting effect on the current economy. The overall judgment among US economists is that the chances of a US recession are low. We expect US GDP growth to remain around 1.4% and 1.8% in this year’s third and fourth quarters, respectively, before recovering to 2% next year. We expect GDP to reach 2.5 percent this year and 2.1 percent next year. The US interest rate cut process will not be too aggressive; the basic process is to cut 25 basis points in September, November, and December, and 25 basis points, a total of 75 basis points this year. It will fall another percentage point next year, for a total of 1.75 percentage points by the end of next year[18]. There is a perception in the market that the US will cut 50 basis points the first time, and 50 basis points the second time. This should not happen because such a rate cut would increase the fear in the market, and the data on the fundamentals of the US economy do not support such a rate cut process. Thus, for the most part, extreme market volatility usually manifests itself as a sharp rise or fall in stock prices over a short time, which can be triggered by a variety of factors, including unexpected changes in company performance, the release of macroeconomic data, statements by policymakers, or even market rumors. For example, a company’s sudden earnings report that beats expectations may cause its share price to rise sharply; Conversely, an adverse policy change could trigger panic selling in the market 2[19-23]. 1. Specific manifestations and causes of extreme fluctuations Unexpected change in the company’s results: The company’s profit or loss exceeded market expectations, causing the stock price to fluctuate wildly. Release of macroeconomic data: Changes in critical economic data, such as GDP, inflation, etc., can affect market sentiment and investment decisions[24].Policymaker’s statement: Policy adjustments by the government or the central bank, such as interest rate changes and monetary policy adjustments, will have a direct impact on the market. Market rumors: Unconfirmed information or rumors can also trigger violent market reactions. Strategies for dealing with extreme volatilityTimely access to and analysis of market information: maintain sensitivity to market dynamics and timely access to and analysis of the latest economic data and policy developments. Develop a flexible investment strategy: Adjust your portfolio to changes in the market and avoid over-concentration in one sector or stock[25].Setting a stop loss or hedging with derivatives: When necessary, take protective measures, such as setting a stop loss or hedging with derivatives, to reduce risk[26]. Through in-depth analysis and reasonable risk management, investors can better navigate the market’s extreme volatility and achieve their investment goals.

## Analysis of Sentiment in Financial Markets

Market sentiment, sometimes referred to as investor sentiment, is not correlated with fundamental changes in the market. Day traders and technical analysts rely on a measure of market sentiment as it influences the metrics used to measure and profit from short-term price movements caused by crowd psychology among active investors. Market sentiment is also essential for contrarian investors who trade in the opposite direction of the consensus[27-32]. For example, if everyone buys a stock, the opposite person will sell it to profit from the rise. Market sentiment is often described as either bearish or bullish. When sentiment is bearish, prices fall. When it’s bullish, the stock price goes up. Sentiment usually drives the stock market, so market sentiment has nothing to do with the fundamental value of a stock. Price changes occur for many reasons beyond what fundamental analysis can infer. Market sentiment shows a wide range of concerns, expectations, and sentiments about the market, while fundamental values are related to actual business performance. In a recent paper published in the Journal of Computational Science, Twitter’s mood predicts the stock market; researchers from Indiana University and the University of Manchester used tweets from Twitter users to analyze two Mood models, OpinionFinder and Google-Profile of Mood States (GPOMS). [33]To capture and analyze changes in public sentiment. Among them, OpinionFinder divided people’s emotions into positive and negative patterns, while GPOMS divided emotions into six more detailed categories: Calm, Alert, Sure, Vital, Kind, and Happy. Using the Granger causality test, the authors find a clear correlation between public sentiment and the Dow Jones Average (DJIA), and the time series of public sentiment can be used as an independent variable of stock index changes. In particular, the Calm index in GPOMS can effectively respond to changes in the index within two to six days in advance[34-35]. Therefore, specific indicators of public sentiment may be effective predictors of future stock price movements. Based on such speculation, the authors of this paper input the public emotion time series as an independent variable into the Self-organizing Fuzzy Neural Network [SOFNN] model based on such an improvement. The effect of prediction has been improved significantly[36]. The model can effectively predict the direction of the rise and fall of the closing price of the DJIA index with an accuracy of 86.7%. In comparison, the average percentage of prediction errors decreased by 6%.

## The Application of Market Sentiment Analysis in Financial Industry

In the financial industry, the application of market sentiment analysis has penetrated multiple segments. For example, investors use sentiment analysis in stock trading to identify overbought or oversold stocks. By monitoring investor sentiment on social media and news sites, analysts can predict the direction of stock prices, allowing them to make more informed trading decisions. Moreover, forex traders rely on market sentiment analysis to predict exchange rate changes in forex trading. They develop more accurate trading strategies by analyzing global economic data, political events and investor sentiment reactions. This approach improves the success rate of transactions and enhances risk management capabilities. In addition, economists also apply market sentiment analysis to macroeconomic analysis to predict economic trends. Economists can reveal changes in the mood of market economic entities by systematically combing through and interpreting various economic indicator reports, studies by authoritative institutions, and discussions on economic topics on the Internet. These changes in sentiment often reflect consumers’ and investors’ expectations about future economic conditions and thus provide strong support for policymaking[37-39]. Understanding market sentiment changes can help adjust economic policy and provide early warning of potential economic risks. In addition to the financial industry, market sentiment analysis is vital in other sectors. Retailers can gain insight into consumers’ shopping needs and preferences by analyzing their shopping reviews and social media feedback. This allows merchants to promptly adjust their merchandise and marketing strategies to meet consumer expectations. In terms of tourism, by analyzing the feedback and comments of tourists, tourism organizations can grasp the needs and satisfaction of tourists to improve the quality of tourism services and tourist experience. [40]Market sentiment analysis provides data support for decision-making in various industries and helps enterprises maintain an advantage in the competition.