1. Report Industry Investment Rating - Not provided in the content 2. Core Viewpoints of the Report - The report introduces the LSTM time - series model to improve the prediction of Treasury bond futures inter - period spreads without expanding the original factor set, finding that introducing time - series modeling can improve return stability and risk control for some varieties, but the sensitivity to time - series information varies among different contracts [38][39][40] - The LSTM model outperforms the DNN model on T and TF contracts, while the DNN model shows stronger prediction ability on TS contracts; the performance of both models on the TL contract is limited due to the short listing time [38][39] - Using the LSTM model's prediction probability for position weighting can enhance the return - risk characteristics of strategies on T, TF, and TS contracts, but the effect is not obvious on the TL contract [38][39] 3. Summary by Relevant Catalogs 3.1 Inter - period Spread Influencing Factors and Indicator Selection - Traditional machine - learning models and DNNs have limitations in time modeling as they assume sample independence in the time dimension and rely on manual feature engineering to introduce time information, and DNNs lack explicit sequence structure [7] - LSTM is an improved form of RNN, introducing a memory unit to decouple long - term information storage and current state output, and using gate - control structures to control information flow, which can better capture long - term dependencies [7] - Treasury bond futures inter - period spreads show trend characteristics in some stages, which are difficult to capture by previous models. Therefore, the LSTM time - series model is introduced to enhance the description of the time - series structure [12] 3.2 Recurrent Neural Network Testing Process 3.2.1 Data Processing and Sample Construction Process - The data includes fundamental factors of T, TF, TS, and TL contracts, and the features are constructed by aligning and introducing capital - related indicators and creating derived spread variables. The label is the first - order difference of the inter - period spread [16][17] - Data pre - processing involves removing early - listing samples and some end - of - month trading days, filtering small - amplitude spread changes, filling missing values, and standardizing data [18] - Time - series samples are constructed using a sliding window with a 5 - trading - day historical window, and samples are divided into training, validation, and test sets by strict time segmentation [19][20] - The model is trained using a weighted cross - entropy loss function and the Adam optimizer, with learning - rate decay and early - stopping mechanisms based on validation - set loss [22] 3.2.2 Parameter Setting and LSTM Network Structure - The task is a binary - classification prediction for the next - trading - day direction change of the inter - period spread, with input as a sequence of factor values over 5 consecutive trading days and output as binary logits [24] - The LSTM network has 3 layers, a hidden - state dimension of 8, and a Dropout ratio of 0.3. It uses the hidden state at the last time - step for classification [25] - The weighted cross - entropy loss is used to address class imbalance, and the Adam optimizer with learning - rate decay is applied for parameter updates [26][27] 3.3 Model Test Results 3.3.1 Comparison of Out - of - Sample Tests between LSTM and DNN Models - On T and TF contracts, the LSTM model has higher cumulative returns, Sharpe ratios, and better drawdown control compared to the DNN model; on the TS contract, the DNN model performs better; on the TL contract, the performance of both models is limited [28][30] 3.3.2 Probability - Weighted Backtesting of the LSTM Model - The prediction probability of the LSTM model is used as the position signal, and the position is normalized and limited to control risks [33] - Probability - weighted strategies improve the out - of - sample performance on most contracts, enhancing returns and Sharpe ratios without significantly increasing drawdowns, but the effect is not obvious on the TL contract [35] 3.4 Conclusion - Introducing the LSTM time - series model can improve the prediction of Treasury bond futures inter - period spreads for some varieties, but the effectiveness depends on contract characteristics, sample coverage, and spread structure [38][39][40] - Using prediction probability for position weighting has potential in improving strategy performance, and future research can further test the applicability of time - series modeling under more complex conditions [39][40]

Core Insights - The report emphasizes the growing importance of AI in asset allocation, particularly in stock price prediction, highlighting the capabilities of AI models like TrendIQ in providing effective analysis and predictions [3][4][10] - It discusses the evolution of predictive models from traditional LSTM to more advanced architectures like Transformers, which offer improved performance in handling complex financial data [39][40] Group 1: AI in Stock Price Prediction - The introduction of AI large models has significantly enhanced the ability to predict stock prices by addressing the limitations of traditional machine learning models, particularly in processing unstructured data [3][4] - TrendIQ is presented as a mature platform that supports both local and web-based deployment, offering advantages in security, speed, and user-friendliness [4][12] Group 2: Model Evolution and Capabilities - The report outlines the transition from LSTM to Transformer architectures, noting that Transformers provide global context awareness and better handling of long-term dependencies, which are crucial for financial predictions [8][39] - It highlights the limitations of LSTM, such as its single modality and weaker interpretability, which can pose risks in a regulated financial environment [7][10] Group 3: TrendIQ Implementation - The implementation of TrendIQ involves a structured process including data preparation, model training, and user interaction through a web application, ensuring a seamless prediction experience [12][20] - The report details the specific Python scripts used in the TrendIQ framework, emphasizing the importance of each component in the overall predictive process [12][18][20] Group 4: Future Directions - Future advancements in AI stock prediction are expected to focus on multi-modal integration, combining visual data from candlestick charts with textual analysis from financial news, enhancing predictive accuracy [40][41] - The report suggests that real-time knowledge integration will further improve the robustness of AI models, allowing them to adapt to changing market conditions dynamically [40][41]

Core Insights - The article discusses the growing importance of artificial intelligence (AI) and machine learning (ML) in the financial industry, highlighting their applications in quantitative trading and risk management, while also addressing the challenges faced when transitioning from academic research to practical implementation [1][2]. Group 1: AI and ML Applications in Finance - AI and ML are increasingly being utilized in various financial applications, but there are significant challenges when these models are applied in real-world scenarios [1][2]. - Financial institutions prioritize decision-making tools that support "What-if" analyses, such as assessing the impact of interest rate changes [5]. - The complexity of financial data, which includes time series, yield curves, and macroeconomic data, poses challenges for traditional models like LSTM [5]. Group 2: Challenges in Implementation - Many discussions around AI and ML remain theoretical, with practical issues often lacking systematic public discourse [2]. - The integration of tools like Jupyter Notebook can hinder engineering management, and compatibility issues between TensorFlow and PyTorch complicate the development of reusable components [5]. - There is a scarcity of professionals who possess expertise in finance, machine learning, and systems engineering, which is critical for successful implementation [5]. Group 3: Educational and Recruitment Initiatives - The article mentions a lecture by Professor Chak Wong from J.P. Morgan's Machine Learning Center of Excellence, focusing on the practical applications of AI/ML in financial institutions [10][11]. - The event also serves as a recruitment session for J.P. Morgan, inviting candidates from various academic backgrounds to engage with a leading international team [11].