Improvement of quantitative trading strategies based on behavioral finance
TSAI
Behavioral finance theory points out that investors are not completely rational in the decision-making process, and their cognitive biases and emotional fluctuations will significantly affect market price trends. In order to deeply analyze these behavioral factors, the TSAI platform has carried out multi-dimensional research and data collection work.
Through in-depth mining of more than 100,000 investor trading account data, we analyze investors’ trading frequency, position holding time, and buying and selling decision-making points in different market environments. Research has found that about 70% of investors will increase their trading frequency by 40% when the market rises for more than 3 consecutive trading days, and 60% of them will increase their positions at this time, which reflects the trading decisions caused by overconfidence. radical. At the same time, natural language processing technology was used to analyze more than 5 million pieces of text data from discussions on financial markets on mainstream financial forums and social media platforms in the past five years to extract market sentiment tendencies. The results show that when the market sentiment index (constructed based on the ratio of positive and negative words in the text) exceeds 80%, the probability of a subsequent sharp market correction within 1 month reaches 70%, indicating that excessive market optimism can easily lead to price overestimation.
Based on an in-depth understanding of investor behavioral factors, the TSAI platform has constructed quantitative trading strategies that counter market sentiment. The core of this strategy is to accurately identify excessive optimism and pessimism in the market and operate in the opposite direction accordingly.
During the strategy construction process, a variety of indicators are comprehensively used. First, in terms of market sentiment indicators, the market sentiment index obtained by combining the above social media text analysis, and the fear index (VIX) calculated by calculating the difference between the implied volatility and historical volatility of the option market. When the market sentiment index is above 85% for 5 consecutive trading days and the VIX index drops 30% from the average value of the past 20 days, the market is determined to be overly optimistic. Secondly, the price deviation indicator calculates the deviation between the current market price and the intrinsic value by constructing an intrinsic value model based on asset fundamental data (such as price-earnings ratio, price-to-book ratio, etc.). A sell signal is triggered when price deviation exceeds 30% and market sentiment is in overly optimistic territory.
In order to verify the effectiveness of the strategy, the TSAI platform uses historical market data of the past 20 years for backtesting, covering multiple markets such as stocks, futures, and foreign exchange. The backtest results show that in the stock market, the selling operations triggered by this strategy during the overly optimistic stage of the market avoided subsequent price drop losses of 20% - 30% on average; the buying operations triggered by the strategy during the overly pessimistic stage of the market, on average, were Achieved revenue growth of 25% - 35% within the next 3 - 6 months. In the futures market, similar operations have also achieved significant results on different varieties, with the average annualized return increasing by 15-20 percentage points compared with traditional trend following strategies.
Financial markets are changing rapidly, and investor behavior patterns are not static. The quantitative trading strategy of the TSAI platform has a dynamic optimization mechanism to ensure that the strategy is always in line with the actual market conditions.
On the one hand, the platform continues to track external factors such as macroeconomic data, industry policy adjustments, and changes in market structure. For example, when macroeconomic data shows that GDP growth slows down for two consecutive quarters, the weight of market sentiment indicators is adjusted to increase its impact in strategic decision-making, because investor sentiment may have a greater impact on market prices at this time. Significantly. On the other hand, based on the feedback data of the strategy in actual transactions, the strategy parameters and model structure are continuously optimized. By analyzing daily transaction data and using optimization technologies such as genetic algorithms, parameters such as transaction thresholds and indicator weights in the strategy are dynamically adjusted.
By deeply integrating behavioral finance theory with quantitative trading strategies, the TSAI platform provides investors with more competitive trading solutions. Amid market uncertainty, we can accurately grasp the trading opportunities brought about by investors' irrational behavior, effectively avoid market risks driven by emotions, and help investors achieve stable returns in the complex and ever-changing financial market.