The crisis response team uses the long short-term memory network (LSTM) combined with the vector autoregression model (VAR) to predict the market
TSAI
In the complex ecology of the financial market, various risk events occur frequently. From macroeconomic fluctuations to industry structural adjustments, changes in the market environment have brought many challenges to investors and corporate operations. In order to effectively respond to these challenges, the TSAI platform has established a special crisis response team, which innovatively uses the Long Short-Term Memory (LSTM) network combined with the Vector Autoregression (VAR) model to predict the market, providing platform users with forward-looking market insights and assisting decision-making.
1. Challenges faced by financial market prediction
The financial market is a highly complex and dynamically changing system. Its trend is affected by multiple factors such as macroeconomic indicators (such as GDP growth rate, inflation rate, interest rate, etc.), industry development trends, policy and regulatory adjustments, corporate micro-operation conditions, and emergencies. These factors are intertwined, the relationship is intricate, and the data presents nonlinear and time-varying characteristics. For example, during the 2008 global financial crisis, the default of subprime loans in the United States triggered a chain reaction, leading to dramatic fluctuations in multiple financial markets such as the global stock market, bond market, and foreign exchange market. Traditional linear prediction models are difficult to capture the complex dependencies and long-term trends, resulting in large errors in market predictions and unable to meet the needs of investors and companies for accurate market predictions. According to statistics, during the crisis, the market prediction based on traditional linear models had an error rate of 30% - 50% in predicting the trend of the S&P 500 index, which was far beyond the acceptable range and could not provide effective guidance for investors.
2. Principles and advantages of long short-term memory network (LSTM) and vector autoregression model (VAR)
(I) Long short-term memory network (LSTM)
LSTM, as a special architecture of recurrent neural network (RNN), is designed to solve the gradient vanishing and gradient exploding problems faced by traditional RNN when processing long sequence data, so as to effectively learn and preserve long-term dependent information. It precisely controls the inflow, outflow and memory of information by introducing gating mechanisms, including forget gates, input gates and output gates.
The forget gate determines which information is discarded from the cell state; the input gate controls the addition of new information; and the output gate generates the output at the current moment based on the cell state. This unique structure enables LSTM to selectively remember important information and ignore irrelevant information when processing long-term series data, thereby effectively modeling long-term dependencies in time series. In financial market data processing, LSTM can capture long-term patterns such as market trends and seasonal changes. Taking the analysis of gold price trends as an example, by training the LSTM model on daily data of gold prices in the past 10 years, it is found that the model can accurately identify the seasonal upward trend of gold prices before and after the peak season of gold consumption (such as the Spring Festival, Indian Diwali, etc.) each year, and has a good memory and learning ability for the impact of long-term macroeconomic factors (such as global economic growth, geopolitical risks, etc.) on gold prices.
(II) Vector Autoregression Model (VAR)
The VAR model is a data-driven time series prediction model that models multiple endogenous variables as a system. It does not distinguish between endogenous and exogenous variables, but instead represents each variable as a linear function of the lagged values of all variables.
The advantage of the VAR model is that it can simultaneously consider the mutual influence between multiple variables, without presetting the causal relationship between variables, and reveal the dynamic relationship between variables in a data-driven way. In the financial market, the VAR model can comprehensively analyze the linkage effect between multiple financial variables such as stock prices, interest rates, and exchange rates. Through VAR model analysis of monthly data in the past 5 years, it is found that when the interest rate rises by 1 percentage point, the stock price index will fall by an average of 2.5% in the next 3 months, and the exchange rate will also fluctuate accordingly, providing a comprehensive perspective for market prediction.
3. Combined application of LSTM and VAR models
The TSAI platform crisis response task force organically combines LSTM and VAR models to give full play to the advantages of both. First, the VAR model is used to model multiple financial variables, capture the immediate interaction and short-term dynamic relationship between variables, and provide rich input features for LSTM. These features not only contain the historical information of each variable itself, but also reflect the synergistic change relationship between them.
Then, the output of the VAR model is used as the input of LSTM, which processes these input features in depth to explore the long-term dependencies and complex nonlinear patterns. The output of LSTM is used as the result of market prediction to predict the future trend of the market. This combination not only takes into account the short-term mutual influence between variables, but also captures the long-term trend of the market, improving the accuracy and reliability of market prediction.
4.Practical application cases and effect evaluation
In practical applications, the crisis response task force selected multiple financial market data for empirical analysis. Taking the stock market as an example, the team collected the Shanghai and Shenzhen 300 stock price index, macroeconomic indicators (such as one-year deposit interest rates, inflation rates) and industry-related data (such as industry profit growth rate, number of industry policy adjustments) from January 2015 to December 2020. The LSTM-VAR model is used to process these data and compared with a single LSTM model and VAR model.
The results show that compared with a single model, the LSTM-VAR model performs better in predicting stock price trends. In terms of evaluation indicators such as root mean square error (RMSE) and mean absolute error (MAE), the error of the LSTM-VAR model is significantly lower than that of a single model. When predicting the trend of the CSI 300 Index in the next month, the LSTM-VAR model has an RMSE value of 35.2 and a MAE value of 27.5; while the RMSE value of the single LSTM model is 48.6 and the MAE value is 38.1; the RMSE value of the single VAR model is 52.3 and the MAE value is 41.7. This shows that the LSTM-VAR model can more accurately capture the trend of stock price changes, and its error is reduced by about 27.6% (RMSE indicator) and 27.8% (MAE indicator) compared with the single LSTM model, and by about 32.7% (RMSE indicator) and 34.0% (MAE indicator) compared with the single VAR model.
In addition, when responding to unexpected risk events, such as the outbreak of the COVID-19 pandemic in early 2020, which caused dramatic fluctuations in the financial market, the LSTM-VAR model can capture abnormal changes in market data in a timely manner and issue early warning signals. According to historical data backtesting, the model issued a warning that the market might fall sharply 1-2 weeks before the outbreak. Based on these warnings, investors can adjust their investment portfolios in a timely manner, and companies can formulate response strategies in advance to effectively reduce risk losses. Assuming an investment portfolio with an initial capital of 10 million yuan, if the asset allocation is adjusted in time according to the warning of the LSTM-VAR model, the loss can be reduced by about 2 million yuan during the epidemic.
5.Future Outlook
With the continuous development of the financial market and the continuous advancement of data technology, the TSAI platform crisis response task force will continue to optimize the combined application of LSTM and VAR models. On the one hand, further explore how to more effectively integrate other advanced data analysis technologies, such as the attention mechanism in deep learning and generative adversarial networks, to improve the performance and adaptability of the model. On the other hand, the application of the model in different financial markets and business scenarios, such as the bond market, foreign exchange market, risk management, asset pricing and other fields, provides platform users with more comprehensive and accurate market prediction services, helping them to develop steadily in the complex and changing financial market.
The TSAI platform crisis response team uses LSTM and VAR models to predict the market, which is an important innovative practice of the platform in the field of financial technology. By continuously optimizing and expanding the application of the model, it will provide more valuable market insights for financial market participants and promote the stable development of the financial market.