Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

Risk management of stock index futures

The Hong Kong Hang Seng index futures is taken as a study object and a method of empirical analysis is adopted in order to verify the validity of the application of the value-at-risk (VaR) method in the risk measurement of the stock index futures market. The results suggest that under normal market conditions it is feasible to apply the VaR method in the measurement of the market risks of stock index futures. The daily VaR value of the stock index futures provides a foreseeable profit and loss of the stock index futures. Financial supervisors can adjust their supervising strategies according to the daily VaR value. The speculators can adjust risk capital reserve rates in the same way. The application of this method in China's stock index futures market requires the solutions to specific problems: the absence of historical data, the difficult confirmation of non-risk interest rates etc.

Cost-benefit analysis of trading strategies in the stock index futures market

With the introduction of many derivatives into the capital market,including stock index futures,the trading strategies in financial markets have been gradually enriched.However,there is still no theoretical model that can determine whether these strategies are effective,what the risks are,and how costly the strategies are.We built an agent-based cross-market platform that includes five stocks and one stock index future,and constructed an evaluation system for stock index futures trading strategies.The evaluation system includes four dimensions:effectiveness,risk,occupation of capital,and impact cost.The results show that the informed strategy performs well in all aspects.The risk of the technical strategy is relatively higher than that of the other strategies.Moreover,occupation of capital and impact cost are both higher for the arbitrage strategy.Finally,the wealth of noise traders is almost lost.

Optimal Hedging Strategies of Stock Index Futures Based on the Perspective of Information Asymmetry

Based on two different risk measurement criteria, this article studied the optimal hedging strategies of stock index futures in the case of asymmetric information, and discussed the influence of insider information on the hedging effect. Through simulation analysis, it can be shown that hedging people with insider information can save hedging costs to a certain extent, which also explains the reason why investors try to obtain corporate information in actual investment activities.

The Different Performance of the Stock Market Indexes of the Three Countries in Different International Events

During the period of different changes in the global situation,the stock indexes of China,the United States,and the United Kingdom all showed different trends.Overall,during the outbreak of the epidemic,they all received a huge impact,and due to the different policies and coping strategies of various countries,the follow-up performance also varies greatly.Brexit has only had a slight impact on the British domestic market in a short period time,and China and the United States have prepared for investment in the new market after Brexit,which has also caused the corresponding market index to perform better before the follow-up.Due to the differences in the main market targets and the differences in the geographical location of countries,the negative impact on the British market was more obvious during the Russia-Ukraine conflict,while the stock indexes of China and the United States were relatively stable and even showed an upward trend.It can be seen from the data analysis that the markets in different countries are affected by time differently.With the growing correlation between the markets of various countries,investors should pay more attention to the global situation and the policy orientation of different countries.Considering risk diversification while taking policy dividends helps to obtain stable returns.

Profit Guided or Statistical Error Guided? A Study of Stock Index Forecasting Using Support Vector Regression

Stock index forecasting has been one of the most widely investigated topics in the field of financial forecasting. Related studies typically advocate for tuning the parameters of forecasting models by minimizing learning errors measured using statistical metrics such as the mean squared error or mean absolute percentage error. The authors argue that statistical metrics used to guide parameter tuning of forecasting models may not be meaningful, given the fact that the ultimate goal of forecasting is to facilitate investment decisions with expected profits in the future. The authors therefore introduce the Sharpe ratio into the process of model building and take it as the profit metric to guide parameter tuning rather than using the commonly adopted statistical metrics. The authors consider three widely used trading strategies, which include a na¨?ve strategy, a filter strategy and a dual moving average strategy, as investment scenarios. To verify the effectiveness of the proposed profit guided approach, the authors carry out simulation experiments using three global mainstream stock market indices. The results show that profit guided forecasting models are competitive, and in many cases produce significantly better performances than statistical error guided models. This implies thatprofit guided stock index forecasting is a worthwhile alternative over traditional stock index forecasting practices.

Stock index adjustments and analysts' forecast optimism:A quasi-natural experiment on the CSI 300 Index

As stock index adjustments comprise a basic system of capital market,their potential influence on analysts’earnings forecasts is worthy of research.Based on a research sample of 23 adjustments to the CSI 300 Index from June 2007 to June 2018 and the backup stocks announced during the same period,this study examines the impact of additions to stock index on analysts’forecast optimism using a staggered difference-in-differences model.The research results show that after stocks are added to the stock index,analysts’earnings forecast optimism about these stocks increases significantly.Cross-sectional analysis indicates that this increase is more significant when the market is bullish,institutional ownership is low,the ratio of listed brokerage firms is low,star analyst coverage is low,firms show seasoned equity offering activity,the ratio of analysts from the top five brokerage firms ranked by commission income is high,and the analysts’brokerage firms are shareholders.However,analystlevel tests find that analysts’ability helps to reduce the impact of additions to stock index on earnings forecast optimism.Furthermore,additions to stock index significantly increase analyst coverage and forecast divergence.Economic consequences tests find additions to stock index significantly increases stock price synchronization,which is partly mediated by analysts’earnings forecast optimism.This study enriches the literature on the impact of basic capital market systems and analyst behavior.The findings suggest that investors should rationally evaluate analysts’earnings forecasts for stocks added to the stock index and obtain further information from various channels to improve asset allocation efficiency.

Stock index adjustments, analyst coverage and institutional holdings: Evidence from China

Using 231 pairs of matched firms from 2009 to 2012 in Chinese stock market,we find that the stock index adjustment significantly affects the analyst coverage, which in addition to the stock index leads to more analyst coverage, while deletion from the stock index has no significant effect, indicating that stock index adjustment can significantly change the information environments of firms that are added to the index. An index adjustment also affects institutional holdings in consideration of new information(e.g., changes in fundamentals and information environments). Changes in institutional holdings are partially due to changes in analyst coverage, and both index funds and other types can change their portfolios in response to changes in the target firms' informativeness.

基于CBAMs-BiLSTM模型的中国股市预测

卷积块注意力模块(CBAM)因其可以有效地提高深度学习模型的预测准确性从而在各种预测问题中显示了其优越性。然而,CBAM在股指预测问题中的有效性研究却十分有限。为了解决这个问题并提高股指的预测精度,本文提出了CBAMs-BiLSTM模型。它将多个CBAM与双向长短期记忆网络(BiLSTM)相结合。研究中,标准指标评价法(SME)和模型置信集检验(MCS)用于综合评价模型的优越性和稳健性。实验数据为具有代表性的中国股指数据集:上证综合指数和深证综合指数。数值结果表明,CBAMs-BiLSTM优于单独的BiLSTM。其中在MAE,RMSE和MAPE上分别平均降低了13.06%,13.39%和12.48%。这证实了CBAM可以有效地提高BiLSTM的预测精度。此外,通过与其他流行模型进行对比,并研究了改变数据集、预测方法和训练集的大小的影响。结果一致证实了CBAMs-BiLSTM在预测精度和投资回报方面的优越性和稳健性。

基于PLUS和InVEST模型的合肥市生态系统碳储量时空演变特征

[目的]为寻求“双碳”目标导向下的合肥市城市发展新方案。[方法]依据合肥市2000-2020年5期土地利用数据,在合肥市国土空间规划指引下,运用PLUS模型模拟得到2035年合肥市土地利用时空演变规律,耦合InVEST模型探究多情景下合肥市碳储量时空变化特征,并进一步挖掘土地综合利用程度对碳储量的影响。[结果](1)2000-2020年合肥市土地利用变化特征主要表现为耕地、林地减少,其中耕地为建设用地扩增主要来源。自然发展和农田资源保护情景的土地变化规律大致相同,主要表现为耕地、林地、水体减少;绿色汇增城市发展情景下,林地相比其余2个情景面积由减少转为增加。(2)2000-2020年合肥市碳储量逐年递减,其中2005-2010年碳损失最为剧烈。到2035年,自然发展情景、农田资源保护情景、绿色汇增城市发展情景碳储量分别为138.96×10^(6),140.13×10^(6),139.81×10^(6) t。农田资源保护情景下,碳储量明显增加区域最低,建设用地扩张减缓;绿色汇增城市发展情景下,林地由碳损失转为碳固持,是最具固碳潜力的发展趋势。(3)绿色汇增城市发展情景土地利用率最高,可有效降低土地综合利用程度对碳储量流失的威胁。[结论]农田资源保护情景与绿色汇增城市发展情景均有助于城市固碳发展,实施生态保护与城市发展并行政策,调整土地综合利用模式有助于改善城市碳流失。

投资者情绪与指数收益——不同市场条件下的差异分析

文章研究了投资者情绪对市场收益率的解释力。基于2011—2020年月度数据,以成交量和投资者信心指数作为投资者情绪指标,检验了其对于指数收益的解释力。结果表明投资者情绪对于股指收益率具有较强解释力。进一步研究根据行情走势将数据分为4个阶段,分别检验不同阶段投资者情绪对于收益率解释力的异同,结论显示成交量和投资者信心指数对于收益率的解释力根据股指处于不同波动阶段而呈现出一定差异。信心指数和成交量作为情绪指标具有一定互补性。此外还检验了投资者信心指数、成交量与收益率之间的内生性,结论表明变量间不存在内生性问题。

Analysis of Financial Derivatives by Mechanical Method (Ⅰ)——Basic Equation of Price of Index Futures

Similar to the method of continuum mechanics, the variation of the price of index futures is viewed to be continuous and regular. According to the characteristic of index futures, a basic equation of price of index futures was established. It is a differential equation, its solution shows that the relation between time and price forms a logarithmic circle. If the time is thought of as the probability of its corresponding price, then such a relation is perfectly coincided with the main assumption of the famous formula of option pricing, based on statistical theory, established by Black and Scholes, winner of 1997 Nobel’ prize on economy. In that formula, the probability of price of basic assets (they stand for index futures here) is assummed to be a logarithmic normal distribution. This agreement shows that the same result may be obtained by two analytic methods with different bases. However, the result, given by assumption by Black_Scholes, is derived from the solution of the differential equation.

Using Feed Forward BPNN for Forecasting All Share Price Index

Use of artificial neural networks has become a significant and an emerging research method due to its capability of capturing nonlinear behavior instead of conventional time series methods. Among them, feed forward back propagation neural network (BPNN) is the widely used network topology for forecasting stock prices indices. In this study, we attempted to find the best network topology for one step ahead forecasting of All Share Price Index (ASPI), Colombo Stock Exchange (CSE) by employing feed forward BPNN. The daily data including ASPI, All Share Total Return Index (ASTRI), Market Price Earnings Ratio (PER), and Market Price to Book Value (PBV) were collected from CSE over the period from January 2nd 2012 to March 20th 2014. The experiment is implemented by prioritizing the number of inputs, learning rate, number of hidden layer neurons, and the number of training sessions. Eight models were selected on basis of input data and the number of training sessions. Then the best model was used for forecasting next trading day ASPI value. Empirical result reveals that the proposed model can be used as an approximation method to obtain next day value. In addition, it showed that the number of inputs, number of hidden layer neurons and the training times are significant factors that can be affected to the accuracy of forecast value.

Model for stock-recruitment dynamics of the Peruvian anchoveta (<i>Eugraulis ringens</i>) off Peru

This paper was aimed at re-examining the validity of the results from Cahuin et al. (Estuar. Coast. Shelf Sci. 84, 2009) and identifying a model to describe the stock-recruitment relationship of the Peruvian anchoveta (Eugraulis ringens). Regression analysis was used to determine if density-dependent effects were present. The analysis did not show the existence of any densitydependent effects. It is important to use environmental factors and take observational and process errors into account when attempting to identify density-dependent effects in fish populations. Sea surface temperature (SST) and Southern Oscillation Index (SOI) were used as independent variables to fit the recruitment dynamics of the anchoveta. Both SST and SOI were found to be significantly important parameters in structuring anchoveta dynamics according to Akaike Information Criterion (AIC) and R2 values. The results of this study do not correlate with the findings of Cahuin et al., (2009), where density-dependent effects and the presence of regimes were detected. In conclusion, the recruitment Rt is essentially determined in proportion to spawning stock biomass St, and then environmental factors in year t further change the recruitments. This mechanism is completely same with that for Japanese sardine proposed by Sakuramoto (The Open Fish. Sci. 5, 2012).

Empirical Test of "Barometer Function" of China's Stock Market

Through the empirical test of the economic and stock market price index from 1994-2001.6, this article finds that the price tendency of the stock market in China could reflect the economic status and the future trend, thus has the function of barometer, additionally through the normal analysis of the continuing falling of the stock price since July 2001, so, the paper comes to the conclusion that the falling price is the reflection of the weak macro economy and the accelerating recession of the industries, and therefore is a warning of the possible worsened economic tendency. Suggestions are to adjust the macro fiscal and financial policy to prevent the economy from recessing. By the way the article conducts some of the primary analyses of punishments against market defiance and reducing state owned shares, thus to clarify some of the unclear concepts and prevent the misleading of economic adjust ment.

The Problems Being Faced for Establishing Index Futures Transaction and the Contract Design

The article deeply and systematically probed into a series of problems being faced for establishing index futures transaction in China. Drawing on the successful experiences of other relative countries and according to Chinese real conditions, the author chooses Shanghai composite index as subject-matter, and has designed an index futures contract that accord with the situation of China.

厚尾随机波动率模型下的沪深300股指期权定价分析

首先,对沪深300股指期权交易数据,采用马尔科夫链蒙特卡洛方法、吉布斯采样方法,利用Eviews软件、WinBUGS软件,进行该期权的定价模型选择和参数估计,并通过BUGS程序语言中的贝叶斯估计法,进行模型参数求解.其次,对该期权进行随机波动率模型及B-S模型的实证分析研究,进而通过分析套利空间、误差因素,得出厚尾随机波动率模型对该期权的定价更为准确合理.