Some Results of Stock Trading Strategy Based on Fuzzy System

In order to better study the stock trend and master the basic rules of stock trading, this paper uses the fuzzy system theory to transform the technology trading strategy in stock into the membership function in fuzzy mathematics, so as to study the trend change of technology trading rules in dynamic stock price with mathematical language. It can be seen from the experimental results that when only the moving average rule is adopted, the stock price trend is relatively flat, and the specific changes of the price cannot be accurately obtained. However, when the bull and bear rule is added, the stock price jumps greatly, so that the changes of the price can be easily captured, so as to make corresponding strategic adjustments to maximize the income.

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.

Hedge fund replication using strategy specific factors

Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction,allowing them to better manage portfolio risk.However,the financial crisis of 2008 has heightened investor sensitivity to the high fees,illiquidity,lack of transparency,and lockup periods typically associated with hedge funds.Hedge fund replication products,or clones,seek to answer these challenges by providing daily liquidity,transparency,and immediate exposure to a desired hedge fund strategy.Nonetheless,although lowering cost and adding simplicity by using a common set of factors,traditional replication products might offer lower risk-reward performance compared to hedge funds.This research explores hedge fund replication further by examining the importance of constructing clones with specific factors relevant to each hedge fund strategy,and then compares the strategy specific clone risk and reward performance against both actual hedge fund performance and hedge fund clones constructed using a more general set of common factors.Testing shows that using strategy specific factors to replicate common hedge fund strategies can offer superior risk-reward performance compared to previous general model clones.

Statistics in Stock Day Trading

Price volatility in stock market brings potential profile positions to the traders. How to predict the direction of the stock market or stock price becomes the primary job for traders＇ trading model. We are looking for the direction of the market in a given timeframe. High-frequency traders will consider the potential profile-out position in millisecond level. Long-term holder will look into month time scale. For most of average traders, the ideal timeframe will be on daily base. In this paper, for a non-news trading day, the author will introduce statistics method to predict the stock prices and bid-ask spread for day trading.

IMPLEMENTATION OF THE TRADE MARK STRATEGY IN FOREIGN TRADE AND ECONOMY

Trade marks are the marks of thecommodities and services. They arealso passes for the enterprises to enterthe market, and can reflect the economicbenefits and credit of the enterprises in acomprehensive manner. With thedevelopment of the market economy and theexpansion of foreign trade and business,trade marks are playing a more and moreimportant role in the economy. To establishits own famous brands as well as to protectthe trade marks more effectively, China willspeed up its steps to implement the

Qinghai's Strategy For Foreign Economic and Trade Development

Qinghai Province in northwest China has recently drawn up its strategy for foreign economic and trade development. The highlights include optimizing its investment climate, developing channels for introducing foreign capital and making good use of foreign funds, the development of natural resources and the reform of its medium-to-large enterprises to improve and benefit the development of its

Baidu index and predictability of Chinese stock returns

A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.

Predicting the daily return direction of the stock market using hybrid machine learning algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.

EXISTENCE OF STOCHASTIC EQUILIBRIUM WITH INCOMPLETE FINANCIAL MARKETS

This paper analyzes the aritrage free security markets and the general equilibrium existence problem for a stochastic economy with incomplete financial markets. Information structure is given by an event tree. This paper restricts attention to purely financial securities. It is assume that trading takes place in the sequence of spot markets and futures markets for securities payable in units of account. Unlimited short selling in securities is allowed. Financial markets may be incomplete: some consumption streams may be impossible to obtain by any trading strategy. Securities may be individually precluded from trade at arbitrary states and dates. The security price process is arbitrage free the dividend process if and only if there exists a stochstic state price (present value) process: the present value of the security prices at every vertex is the present value of their dividend and capital values over the set of immediate successors; the current value of each security at every vertex is the present value of its future dividend stream over all succeeding vertices. The existence of such an equilibrium is proved under the following condition: continuous, weakly convex, strictly monotone and complete preferences, strictly positive endowments and dividends processes.

The Business Cycle and Profitability of Trading Strategies

Recent studies show that investor participation in the stock market rises during economic expansion and drops in economic recession. When investor participation is high, investors＇ cognitive and behavioral biases are likely to have a strong influence on stock prices. We consider four trading strategies that are based on well-known market anomalies and examine their profitability under different economic conditions. For all four strategies, the portfolios that are formed in the months when the economy is expanding obtain significant profits, whereas the portfolios formed in economic recession months are not profitable. This finding is robust to different ways of classifying recession months.

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.

An Efficient Algorithm for the Optimal Market Timing over Two Stocks

In this paper, the optimal trading strategy in timing the market by switching between two stocks is given. In order to deal with a large sample size with a fast turnaround computation time, we propose a class of recursive algorithm. A simulation is given to verify the effectiveness of our method.