6,414 research outputs found
Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HSI
The prediction of a stock market direction may serve as an early
recommendation system for short-term investors and as an early financial
distress warning system for long-term shareholders. Many stock prediction
studies focus on using macroeconomic indicators, such as CPI and GDP, to train
the prediction model. However, daily data of the macroeconomic indicators are
almost impossible to obtain. Thus, those methods are difficult to be employed
in practice. In this paper, we propose a method that directly uses prices data
to predict market index direction and stock price direction. An extensive
empirical study of the proposed method is presented on the Korean Composite
Stock Price Index (KOSPI) and Hang Seng Index (HSI), as well as the individual
constituents included in the indices. The experimental results show notably
high hit ratios in predicting the movements of the individual constituents in
the KOSPI and HIS.Comment: in International Journal of Business Intelligence and Data Mining,
201
Neural networks for stock price prediction
Due to the extremely volatile nature of financial markets, it is commonly
accepted that stock price prediction is a task full of challenge. However in
order to make profits or understand the essence of equity market, numerous
market participants or researchers try to forecast stock price using various
statistical, econometric or even neural network models. In this work, we survey
and compare the predictive power of five neural network models, namely, back
propagation (BP) neural network, radial basis function (RBF) neural network,
general regression neural network (GRNN), support vector machine regression
(SVMR), least squares support vector machine regresssion (LS-SVMR). We apply
the five models to make price prediction of three individual stocks, namely,
Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and
average absolute percentage error as criteria, we find BP neural network
consistently and robustly outperforms the other four models.Comment: 13 pages, 3 figures, 4 table
Forecasting the U.S. Real House Price Index
The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007
global financial crisis and revived the interest about forecasting such
imminent threats for economic stability. In this paper we propose a novel
hybrid forecasting methodology that combines the Ensemble Empirical Mode
Decomposition (EEMD) from the field of signal processing with the Support
Vector Regression (SVR) methodology that originates from machine learning. We
test the forecasting ability of the proposed model against a Random Walk (RW)
model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model.
The proposed methodology outperforms all the competing models with half the
error of the RW model with and without drift in out-of-sample forecasting.
Finally, we argue that this new methodology can be used as an early warning
system for forecasting sudden house prices drops with direct policy
implications.Comment: 27 page
Turnover Prediction Of Shares using Data Mining techniques : A Case Study
Predicting the turnover of a company in the ever fluctuating Stock market has
always proved to be a precarious situation and most certainly a difficult task
in hand. Data mining is a well-known sphere of Computer Science that aims on
extracting meaningful information from large databases. However, despite the
existence of many algorithms for the purpose of predicting the future trends,
their efficiency is questionable as their predictions suffer from a high error
rate. The objective of this paper is to investigate various classification
algorithms to predict the turnover of different companies based on the Stock
price. The authorized dataset for predicting the turnover was taken from
www.bsc.com and included the stock market values of various companies over the
past 10 years. The algorithms were investigated using the "R" tool. The feature
selection algorithm, Boruta, was run on this dataset to extract the important
and influential features for classification. With these extracted features, the
Total Turnover of the company was predicted using various classification
algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression.
This prediction mechanism was implemented to predict the turnover of a company
on an everyday basis and hence could help navigate through dubious stock market
trades. An accuracy rate of 95% was achieved by the above prediction process.
Moreover, the importance of stock market attributes was established as well
Forecasting electricity prices with machine learning: Predictor sensitivity
Purpose: Trading on electricity markets occurs such that the price settlement
takes place before delivery, often day-ahead. In practice, these prices are
highly volatile as they largely depend upon a range of variables such as
electricity demand and the feed-in from renewable energy sources. Hence,
accurate forecasts are demanded.
Approach: This paper aims at comparing different predictors stemming from
supply-side (solar and wind power generation), demand-side, fuel-related and
economic influences. For this reason, we implement a broad range of non-linear
models from machine learning and draw upon the information-fusion-based
sensitivity analysis.
Findings: We disentangle the respective relevance of each predictor. We show
that external predictors altogether decrease root mean squared errors by up to
21.96%. A Diebold-Mariano test statistically proves that the forecasting
accuracy of the proposed machine learning models is superior.
Originality: The benefit of adding further predictors has only recently
received traction; however, little is known about how the individual variables
contribute to improving forecasts in machine learning
A dynamic hybrid model based on wavelets and fuzzy regression for time series estimation
In the present paper, a fuzzy logic based method is combined with wavelet
decomposition to develop a step-by-step dynamic hybrid model for the estimation
of financial time series. Empirical tests on fuzzy regression, wavelet
decomposition as well as the new hybrid model are conducted on the well known
index financial time series. The empirical tests show an efficiency of
the hybrid model.Comment: 15 pages, 15 figures, 2 table
Temporal Relational Ranking for Stock Prediction
Stock prediction aims to predict the future trends of a stock in order to
help investors to make good investment decisions. Traditional solutions for
stock prediction are based on time-series models. With the recent success of
deep neural networks in modeling sequential data, deep learning has become a
promising choice for stock prediction. However, most existing deep learning
solutions are not optimized towards the target of investment, i.e., selecting
the best stock with the highest expected revenue. Specifically, they typically
formulate stock prediction as a classification (to predict stock trend) or a
regression problem (to predict stock price). More importantly, they largely
treat the stocks as independent of each other. The valuable signal in the rich
relations between stocks (or companies), such as two stocks are in the same
sector and two companies have a supplier-customer relation, is not considered.
In this work, we contribute a new deep learning solution, named Relational
Stock Ranking (RSR), for stock prediction. Our RSR method advances existing
solutions in two major aspects: 1) tailoring the deep learning models for stock
ranking, and 2) capturing the stock relations in a time-sensitive manner. The
key novelty of our work is the proposal of a new component in neural network
modeling, named Temporal Graph Convolution, which jointly models the temporal
evolution and relation network of stocks. To validate our method, we perform
back-testing on the historical data of two stock markets, NYSE and NASDAQ.
Extensive experiments demonstrate the superiority of our RSR method. It
outperforms state-of-the-art stock prediction solutions achieving an average
return ratio of 98% and 71% on NYSE and NASDAQ, respectively.Comment: Transactions on Information Systems (TOIS
Predicting the direction of stock market prices using random forest
Predicting trends in stock market prices has been an area of interest for
researchers for many years due to its complex and dynamic nature. Intrinsic
volatility in stock market across the globe makes the task of prediction
challenging. Forecasting and diffusion modeling, although effective can't be
the panacea to the diverse range of problems encountered in prediction,
short-term or otherwise. Market risk, strongly correlated with forecasting
errors, needs to be minimized to ensure minimal risk in investment. The authors
propose to minimize forecasting error by treating the forecasting problem as a
classification problem, a popular suite of algorithms in Machine learning. In
this paper, we propose a novel way to minimize the risk of investment in stock
market by predicting the returns of a stock using a class of powerful machine
learning algorithms known as ensemble learning. Some of the technical
indicators such as Relative Strength Index (RSI), stochastic oscillator etc are
used as inputs to train our model. The learning model used is an ensemble of
multiple decision trees. The algorithm is shown to outperform existing algo-
rithms found in the literature. Out of Bag (OOB) error estimates have been
found to be encouraging. Key Words: Random Forest Classifier, stock price
forecasting, Exponential smoothing, feature extraction, OOB error and
convergence
Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling
In this work, we design a machine learning based method, online adaptive
primal support vector regression (SVR), to model the implied volatility surface
(IVS). The algorithm proposed is the first derivation and implementation of an
online primal kernel SVR. It features enhancements that allow efficient online
adaptive learning by embedding the idea of local fitness and budget maintenance
to dynamically update support vectors upon pattern drifts. For algorithm
acceleration, we implement its most computationally intensive parts in a Field
Programmable Gate Arrays hardware, where a 132x speedup over CPU is achieved
during online prediction. Using intraday tick data from the E-mini S&P 500
options market, we show that the Gaussian kernel outperforms the linear kernel
in regulating the size of support vectors, and that our empirical IVS algorithm
beats two competing online methods with regards to model complexity and
regression errors (the mean absolute percentage error of our algorithm is up to
13%). Best results are obtained at the center of the IVS grid due to its larger
number of adjacent support vectors than the edges of the grid. Sensitivity
analysis is also presented to demonstrate how hyper parameters affect the error
rates and model complexity.Comment: 34 Page
Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting
The paper examines the potential of deep learning to support decisions in
financial risk management. We develop a deep learning model for predicting
whether individual spread traders secure profits from future trades. This task
embodies typical modeling challenges faced in risk and behavior forecasting.
Conventional machine learning requires data that is representative of the
feature-target relationship and relies on the often costly development,
maintenance, and revision of handcrafted features. Consequently, modeling
highly variable, heterogeneous patterns such as trader behavior is challenging.
Deep learning promises a remedy. Learning hierarchical distributed
representations of the data in an automatic manner (e.g. risk taking behavior),
it uncovers generative features that determine the target (e.g., trader's
profitability), avoids manual feature engineering, and is more robust toward
change (e.g. dynamic market conditions). The results of employing a deep
network for operational risk forecasting confirm the feature learning
capability of deep learning, provide guidance on designing a suitable network
architecture and demonstrate the superiority of deep learning over machine
learning and rule-based benchmarks.Comment: Within the "equal" contribution, Yaodong Yang contributed the core
deep learning algorithm along with its experimental results, and the first
draft of the manuscript (including Figure 1,2,3,4,7,8,9,11, and Table 3
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