One of many applications of artificial neural networks is discovering non-linear patterns in time series data. In this paper, analysis of the efficacy of applying an artificial neural network to the time series data p...
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ISBN:
(纸本)9783319045733;9783319045726
One of many applications of artificial neural networks is discovering non-linear patterns in time series data. In this paper, analysis of the efficacy of applying an artificial neural network to the time series data produced by fluctuating stock prices is discussed in more detail. There are few current models that are capable of analyzing stocks but they lack in predicting effectively. Results of this neural network are examined through the generated return on investment. The network used for stock analysis is a four layer, feed-forward artificial neural network. The results of this network reveal that artificial neural networks are capable of performing technical analysis on stock prices. The return on investment ranged anywhere from 0.8 to 5.28 % per month or as extrapolated over a year, as high as 17 %.
Most study on online portfolio selection algorithm focus on the theoretical derivation of optimal regret bound or empirically validates portfolio cumulative return and its variability. This study investigates the beha...
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Most study on online portfolio selection algorithm focus on the theoretical derivation of optimal regret bound or empirically validates portfolio cumulative return and its variability. This study investigates the behavior of algorithm under financial crisis based on 2008 stock trading in Bursa Malaysia, a market in small open economy whereby trading actions could not exert impact to the spillover trends from US and Europe. The equity returns data generating process under this scenario is an AR process with positive lag as such algorithms arbitrate between relative growths like Anticorrelation, constant rebalancing are not performing. Whereas algorithms that search for optimal portfolio at each transaction such as Universal Portfolio, Convex Optimization approaches are able to reverse the downward trends of portfolio before market recovery, dampen downside variability and deliver lower extreme returns. We also explained the expendability and practicality of the Convex Optimization approach for future development of automated trading scheme.
The introduction of new technologies and local energy trading concepts in distribution grids are on a rise. However, even though significant progress is being made, there is no single best solution applicable across a...
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ISBN:
(纸本)9781665432993
The introduction of new technologies and local energy trading concepts in distribution grids are on a rise. However, even though significant progress is being made, there is no single best solution applicable across all locations. To ensure benefits, concepts must find balance considering hardware and software requirements, user-friendliness, and provided functionalities. Further, due to the diverse regulatory and economic landscapes in Europe, feasibility is not granted, and concepts have to be tailor-made considering the applicable regulatory provisions. In this paper we present a novel trading concept based on supply function equilibrium (SFE) called approximated automated SFE trading algorithm (ASFET). The concept is designed for automated electrical energy trading in middle and low voltage distribution grids. The algorithm could ensure better calculation performance, easier implementation, and scalability. The aspects of the presented concept are discussed in the paper.
Financial price series trend prediction is an essential problem which has been discussed extensively using tools and techniques of economic physics and machine learning. Time dependence and volatility issues in this p...
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Financial price series trend prediction is an essential problem which has been discussed extensively using tools and techniques of economic physics and machine learning. Time dependence and volatility issues in this problem have made Hidden Markov Model (HMM) a useful tool in predicting the states of stock market. In this paper, we present an approach to predict the stock market price trend based on high-order HMM. Different from the commonly used first-order HMM, short and long-term time dependence are both considered in the high order HMM. By introducing a dimension reduction method which could transform the high-dimensional state vector of high-order HMM into a single one, we present a dynamic high-order HMM trading strategy to predict and trade CSI 300 and S&P 500 stock index for the next day given historical data. In our approach, we make a statistic of the daily returns in the history to demonstrate the relationship between hidden states and the price change trend. Experiments on CSI 300 and S&P 500 index illustrate that high-order HMM has preferable ability to identify market price trend than first-order one. Thus, the high-order HMM has higher accuracy and lower risk than the first-order model in predicting the index price trend. (C) 2018 Elsevier B.V. All rights reserved.
作者：
Ruiz-Cruz, RiemannITESO
Dept Math & Phys Res Lab Optimal Design Devices & Adv Mat OPTIMA Periferico Sur Manuel Gomez Morin 8585 Tlaquepaque 45604 Jalisco Mexico
In the present paper, a mathematical model for a portfolio is proposed. This model is valid for operations of buying and selling shares of an asset in constant periods of time, additionally, it has a state space form ...
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In the present paper, a mathematical model for a portfolio is proposed. This model is valid for operations of buying and selling shares of an asset in constant periods of time, additionally, it has a state space form which can be used to design a control law using control theory. The designed control law can be interpreted as a trading signal to reach a portfolio value desired. The mathematical model and control law proposed are validated by means simulations using real daily prices of Mexican stock exchange. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
This research has two objectives: (1) to model and analyze the momentum effect and (2) to propose a portfolio-reconstruction algorithm that uses the momentum effect to obtain excess return. The momentum effect tends t...
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This research has two objectives: (1) to model and analyze the momentum effect and (2) to propose a portfolio-reconstruction algorithm that uses the momentum effect to obtain excess return. The momentum effect tends to be present in the stock market and describes the phenomenon whereby rising (declining) stocks tend to continue to rise (decline). However, because existing research does not separate momentum effects from stock price fluctuations, it is not always possible to obtain an excess return when working with an unknown dataset that contains a momentum effect. In this research, we define a new external-force momentum-effect (EFME) model based on bias in stock price rises (declines). We prepared an artificial stock dataset that contained this momentum effect and constructed a portfolio with the proposed algorithm. Then, we analyzed the relationship between the EFME model and excess return and verify that excess return is obtained. Additionally, we confirmed that the proposed method yields higher excess return than the existing method when applied to artificial and real stock datasets.
Anchoring is a term used in psychology to describe the common human tendency to rely too heavily (anchor) on one piece of information when making decisions. Here a trading algorithm inspired by biological motors, intr...
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Anchoring is a term used in psychology to describe the common human tendency to rely too heavily (anchor) on one piece of information when making decisions. Here a trading algorithm inspired by biological motors, introduced by L. Gil [2007], is suggested as a testing ground for anchoring in financial markets. An exact solution of the algorithm is presented for arbitrary price distributions. Furthermore the algorithm is extended to cover the case of a market neutral portfolio, revealing additional evidence that anchoring is involved in the decision making of market participants. The exposure of arbitrage possibilities created by anchoring gives yet another illustration on the difficulty proving market efficiency by only considering lower order correlations in past price time series.
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