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The relevance of risk preference and forecasting accuracy for investor survival has recently been the focus of a series of theoretical and simulation studies. At one extreme, it has been proven that risk preference can be entirely irrelevant (Sandroni in Econometrica 68:1303–1341, 2000; Blume and Easley in Econometrica 74(4):929–966, 2006). However...
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Citations
... Yang and Chen (2018) explores the model with different types of agents. Tsao and Huang (2018) explores the issue of survivability and market efficiency, and Huang and Tsao (2018) investigates the effect of evolutionary frequency on forecasting accuracy. ...
Prediction Markets (PMs) are markets in which agents trade event contingent assets. Enterprises use PMs to forecast revenues and project deadlines. This paper presents an Agent-based model, called Logarithmic Market Scoring Rule-Automated Market Maker (LMSR-ASM), to evaluate Prediction Markets. Our model is capable of testing different types of Automated Market Makers (AMMs), which are mathematical functions or computational mechanisms needed to provide liquidity in Prediction Markets. The model offers insights into how to set parameters in a PM and how profits react to contrasting settings and AMMs. In addition, we simulate different probability processes, distinct AMMs, and agent behaviors. This paper also utilizes the LMSR-ASM to evaluate the impact of choosing initial prices in profits and revenue opportunities regarding AMM computational implementation. We show that we can use the LMSR-ASM to find optimal parameters for maximizing profits in PMs and how different AMMs affect market results under a variety of settings.
... The relatively less inefficient markets are mainly located in Europe and America, and the relatively more inefficient mainly in the Middle East [66]. To relate the research on survivability to issues with respect to the efficient markets hypothesis, it is better to endow agents with the ability to forecast market prices Multiscale entropy and dividends [95]. In mathematical finance the efficient market hypothesis is formulated as the martingale property of price processes of tradable assets such as stocks [27,32,44,92]. ...
The multi-fractal analysis has been applied to investigate various stylized facts of the financial market including market efficiency, financial crisis, risk evaluation and crash prediction. This paper examines the daily return series of stock index of NASDAQ stock exchange. Also, in this study, we test the efficient market hypothesis and fractal feature of NASDAQ stock exchange. In the previous studies, most of the technical analysis methods for stock market, including K-line chart, moving average, etc. have been used. These methods are generally based on statistical data, while the stock market is in fact a nonlinear and chaotic system which depends on political, economic and psychological factors. In this research we modeled daily stock index in NASDAQ stock exchange using ARMA-GARCH model from 2000 until the end of 2016. After running the model, we found the best model for time series of daily stock index. In next step, we forecasted stock index values for 2017 and our findings show that ARMA-GARCH model can forecast very well at the error level of 1%. Also, the result shows that a correlation exists between the stock price indexes over time scales and NASDAQ stock exchange is efficient market and non-fractal market.
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This working review shows recent agent-based models (ABMs) for financial market (artificial market simulations) to discuss financial regulations and/or rules. This review aimed to introduce recent papers as many as possible. See [Mizuta 20a] for more details of importance of discussion into design financial markets with artificial market models, contribution to society and how to build and use such models. ([Mizuta 20a] has related materials, detail presentation slides and a presentation movie on YouTube.) It is very difficult to discuss about changing financial market regulations and/or rules by only using results of empirical studies. An artificial market, which is a kind of an agent-based model, can isolate the pure contribution of changing the regulations to the price formation and can treat situations that have never occurred. These are strong points of the artificial market simulation study. Recently, some artificial market studies contributed to discussion what financial regulations and rules should be, for example, price variation limits and short selling regulation whether preventing bubbles and crushes or not, tick size, usage rate of dark pools, rules for investment diversification, speed of order matching systems on financial exchanges, frequent batch auctions, how active funds that trade infrequently make a market more efficient, an interaction between leveraged ETF markets and underlying markets and micro-foundation of price variation model using intelligence of artificial market simulation studies. I will review those studies. 1. Artificial Market Simulation This working review shows recent agent-based models (ABMs) for financial market (artificial market simulations) to discuss financial regulations and/or rules. This review aimed to introduce recent papers as many as possible. See [Mizuta 20a] for more details of importance of discussion into design financial markets with artificial market models, contribution to society and how to build and use such models. ([Mizuta 20a] has related materials, detail presentation slides and a presentation movie on YouTube.) [Mizuta 20a] described that 'designing a financial market that works well is very important for developing and maintaining an advanced economy, but is not easy because changing detailed rules, even ones that seem trivial, sometimes causes unexpected large impacts and side effects. A computer simulation using an agent-based model can directly treat and clearly explain such complex systems where micro processes and macro phenomena interact.' It is very difficult to discuss about changing financial market regulations and/or rules only by results of empirical studies. Because so many factors cause price formation in actual markets, an empirical study cannot isolate the pure contribution of existing new type regulations or of changing rules to price formation. Furthermore, empirical studies cannot investigate situations that have never occurred before in real financial markets. We usually discuss whether regulations should be changed or not on the basis of their effects on price formation. An artificial market, which is a kind of a multi-agent simulation (an agent-based model), can isolate the pure contribution of changing the regulations to the price formation and can treat situations that have never occurred [LeBaron Contact: mizutata@gmail.com https://mizutatakanobu.com 06, Chakraborti 11, Chen 12, Cristelli 14, Todd 16, Mizuta 20a]. These are strong points of the artificial market simulation study. Not only academies but also financial regulators and stock exchanges are recently interested in multi-agent simulations such artificial market models to investigate regulations and rules of financial markets. Indeed, the Science article [Battiston 16] described that 'since the 2008 crisis, there has been increasing interest in using ideas from complexity theory (using network models, multi-agent models, and so on) to make sense of economic and financial markets', and the Nature article [Farmer 09] described that 'such (agent-based) economic models should be able to provide an alternative tool to give insight into how government policies could affect the broad characteristics of economic performance , by quantitatively exploring how the economy is likely to react under different scenarios'. [Aruka 17, Mizuta 20a] also claimed importance of an artificial market simulation. Recently, some artificial market studies contributed to discussion what financial regulations and rules should be [Todd 16, Mizuta 20a], for example, price variation limits and/or short selling regulation whether preventing bubbles and crushes or not [Yagi 10, Yeh 10, Mizuta 13b, Mizuta 15b, Mizuta 16c, Veld 16, Zhang 16, Llacay 19, Xiong 0], the rule for investment diversification [Yagi 17], transaction taxes [Westerhoff 08, Veryzhenko 17], financial leverages [Thurner 12, Veld 16], circuit breakers [Kobayashi 11, Muranaga 99], tick size [Darley 07, Mizuta 13a, Col-lver 17, Yang 20, Zhao 20, Mizuta 22a], frequent batch auctions [Mizuta 16a], usage rate of dark pools [Mo 13, Mizuta 14, Mizuta 15c], speed of order matching systems on financial exchanges [Mizuta 15a, Mizuta 16d], the effects of different regulatory policies directed towards high frequency
The financial stock market is highly complex, nonlinear and uncertain, which makes it difficult to predict price fluctuation. With the advent of the era of artificial intelligence, a variety of intelligent optimization algorithms are constantly applied to the prediction of the stock market. The purpose of this study is to use a support vector machine regression model optimized by an intelligent fuzzy algorithm to predict the situation of the securities market. In this study, the stock price information of sh600060hisense electric equipment from June 2019 to December 2019 was used as the experimental data. As the input parameters of regression models, the starting price, the maximum price, the lowest price, the stock price, the transaction quantity, and the transaction quantity are taken up, and the fuzzy logic is used to make the sample data fuzzy, and the kernel function and optimization parameter are chosen. Then, the obtained data are trained in MATLAB, and the obtained data are effectively classified, and the stock price prediction of the financial market is obtained. The results show that the optimal parameters of the support vector machine regression model of stock data are C = 100, y = 0.01, ɛ= 0.01, and the accuracy of FSVM is about 0.75, which is higher than that of the SVM model (0.71), the square root mean square error (RMSE) is about 0.12, and the average absolute error (MAE) is about 0.015, According to the data, it can be said that the prediction results of the model are effective for the selected seven stocks one-minute data. It is concluded that the fuzzy support vector machine improves the prediction accuracy of the stock market. This study contributes to the prediction of an intelligent algorithm in the stock market.
