neural network in finance and investing pdf

Neural networks (NNs) can be used for prediction, classification, and association problems in various problem areas. Finance and investing is the second. Neural Comput & Applic () 9 Springer-Verlag London Limited Neural Computing & Applications The Impact of Neural Networks in Finance P.R. B. understanding of financial machine learning. Machine learning thrives in data-rich envi- ronments. Models like neural networks are. ETHEREUM NETWORK STATUS GITHUB

Diamond et al. In effect, inability of a NN to provide expla- neural network system for tactical asset allocation nations of how and why conclusions may be a major in seven major bonds markets. For each market, restriction to their use in modelling techniques. Fur- they found that a NN captures the underlying thermore, there is no formal theory for determining relationships in the data, and thus provides the optimal network topology.

Therefore, decisions such best portfolio. The authors proposed that the neural- as the appropriate number of layers and middle layer portfolio outperforms the benchmark by a factor of nodes must be determined using experimentation.

They also noted that small changes in the The development and interpretation of NNs require network design, learning times, and initial conditions expertise and experience, and the training of the may produce large changes in the network network can be computationally intensive. It was behaviour. Debt Risk Assessment decisions than the human underwriters. The system offered an economic benefit by reduced processing The ability to assess risk in the financial market, is costs and risk, and offered an economic gain by an area of paramount importance in the real world improved consistency in underwriting judgements.

This area is lacking a well defined model A different aspect of the problem arises for the or theory, thus it can be difficult to apply either network, not in automating the human decision- conventional mathematical techniques or standard making process, but rather from the use of the AI approaches e.

A N N can network to improve on the quality of the decisions be a useful tool for the domain of risk assessment through its ability to learn to estimate some measure as it does not require a prior specification or a of the risk of a loan applicant's defaulting on his functional domain model.

Reilly et al. The authors were able to compare the per- Burgress [30] investigated the use of neural-nets formance of neural-nets and regression model by for loan risk analysis. The objective was to identify applying the same data set and variables. They and quantify the business benefits which accrue from found that the NNs consistently outperformed the using the powerful modelling capability of NNs, as regression models in predicting bond ratings from opposed to the more established linear regression the given set of financial ratios.

Both in training methodology which underlies most current 'score- and learning samples, the total squared errors for card' systems. He found that the NN exploits non- regression analysis were about an order of magni- linearities and interaction efforts within the data to tude higher than those of a NN. Furthermore, the outperform consistently, an equivalent linear success rates of prediction for NNs were consider- regression 'scorecard' by identifying customers ably higher than for regression analysis.

The success whose applications would normally be rejected as rate of neural-nets was In a similar study, resent an acceptable level of risk. The authors stated that a NN may be a more powerful classification technique if the mappings of layers are carried out in the appropriate manner. Security Market Applications However, they believe that both methods can be applied fairly, and to their best advantage, in order The use of NNs to detect the mysteries of price to obtain accurate results.

The traditional management and is having a profound effect in approaches have limits in their ability to predict The Impact of Neural Networks in Finance price movements. NN approaches in this area have improve the predictability of stock price perform- shown considerable improvements. However, not all the NN researches gave Kimoto et al. White [7], in his study of IBM for when to buy and sell stocks. The prediction Daily Stock Returns, used a standard single hidden- system made an excellent profit in a simulation layer network.

The author reported his disappoint- exercise. The authors concluded that the NN ment in the failure of the simple network in finding approach is more effective than the traditional mul- evidence against the simple EMH. The author found tiple regression.

This, along with pattern recognition, that to obtain evidence against efficient markets with has achieved some interesting insights in financial a simple network is not an easy task. Even simple market research. Bosarge [32] detected a new class networks are capable of misleadingly overfitting an of inefficiencies in the liquid capital markets by asset price series.

He reported that the technology had the ability to predict price movements associated with these inefficiencies. Financial Forecasting Bergerson and Wunsch [33] point out that rule- based approaches are lacking in the flexibility to easily deal with the recognition of these poorly Forecasting is another area that has been identified defined patterns, and unaided neural networks are as one of the most promising applications of arti- better at pattern recognition in a theoretical sense.

Similar approaches in They are good at doing things that are naturally financial forecasting have been applied by a number well handled by rules, such as risk management. It of other researchers. They showed that but it is the combination of this capability, together neural-nets can be used for time series forecasting, with a rule-based system, that makes a useful real- at least for a single period forecasting problem.

The world investment system. Kamijo and Tanigawa [34] authors tested and compared a series data sample applied recurrent neural-nets to recognition of stock containing annual, quarterly, and monthly obser- price patterns. Sixteen experiments were accom- vations using NN models and traditional Box- plished, and they confirmed that the test pattern was Jenkins forecasting models.

The simple neural net- accurately recognised in 15 out of the 16 experi- work models tested on this data could forecast about ments. Tests were based on one particular set returns prediction on a month-on-month basis. The of learning parameters and one architecture. Sharda application performed quantitative asset allocation and Patil [37] expanded their research by investigat- between bond markets and USA cash dollars , and ing a forecasting competition between their neural- achieved returns significantly higher than any indus- net model and Box-Jenkins forecasting.

They found try benchmark. Assets were allocated in seven mar- that NNs provided a robust forecasting in cases kets USA, Japan, UK, Germany, Canada, France of irregular time series, and thus offer promising and Australia which were chosen on the basis of alternative approach to time series forecasting. Each market was modelled on an Moreover, their ability to forecast in a fuzzy sense individual basis using local e. However, these systems are not always cious to non-precious metals.

The system was div- successful as Refenes et al. In the second stage the results for in a controlled simulation experiment and in a real the individual markets were integrated in the global application. They confirmed that DLS is a more portfolio. The result of the study indicated that the robust procedure for 'weakly' non-stationary data neural-portfolio clearly outperformed the benchmark series. The forecasting of inflation rate and exchange Yoon and Swales [36], in their study of predicting rate prediction is another area where NNs have stock price performance, indicated that the NN tech- been successfully applied.

Zwol and Bots [40], nique together with the MDA can significantly experimented with neural-nets in forecasting the P. Folarin German inflation rate. A data set with German 3. Conclusions economic variables were built into a feed-forward backpropagation network.

The results were prom- Artificial neural networks represent a radically dif- ising. However, they found that NNs are most ferent form of computation from the more common sensitive to changes in the learning rate. A learn- algorithmic models. Neural computation is concep- ing rate of 0. Only one tually, massively parallel, typically employing from of nine networks that were trained with a learning several thousand, to potentially many millions of rate of 0. This NN technology The network also appeared sensitive to changes can deliver performance that is similar to, or much in the initial weight range.

Nevertheless, Refenes better than, the conventional problem-solving et al. The unique be obtained if multi-layer perceptron networks are learning capabilities of neural networks promise used in a non-trivial application in the forecasting benefits in many areas of finance, and offers great of currency exchange rates. In their experiment, potential for improvements in productivity and the network delivered an accurate prediction, mak- efficiency. Many of the authors who have of the performance of neural-nets and conventional used the technology reported that its use, in different methods in forecasting time series.

The authors financial areas, can provide positive outcomes. Neu- experimented with three time series of differing ral networks are now well established in the field complexity using different feed-forward, backpro- of academic research and commercial exploitation. They offer particular benefits to modelling tasks Their experiments demonstrated that, for time ser- where little or no a p r i o r i knowledge is available.

However, for series with short have outperformed linear statistical approaches, memory, neural-nets outperform the Box-Jenkins econometric models, and other conventional methods model. Neural networks are robust, parsimonious in a large number of applications and financial in their data requirements, and provide good long problem domains.

Unfortunately, not all the experi- term forecasting. The experiments with neural-nets in forecasting Some authors [7,6,] reported the inability of are not all positive. Fishwick [43], reported that the NN technology to deliver in certain experiments. Burgess et al. It is therefore important to recognise even for inherently non-linear problems, a linear that neural network technology has its own draw- model with error-feedback terms can out-perform backs.

Thus, Neural networks can identify important decision- the performance of both linear and non-liner mod- making factors that appear to be irrelevant, or even els can be considerably improved by the use of factors that conflict with traditional theories in the error-feedback terms in financial time series fore- knowledge domain. Since the scope of training is casting. Marquez et al. Furthermore, most neural networks lack accuracy by using functional forms such as the explanatory capability.

Thus, justifications for results linear model, the logarithmic model and the are difficult to obtain because their connection reciprocal model. The authors reported that neural- weights do not usually have obvious interpretations. However, they con- logic behind specific decisions. Therefore, it is not cluded that one cannot build a neural-net and possible to check intermediate computations or to always expect it to perform best.

Freedman RS. AI on Wall Street. IEEE Expert ; training examples should be readily available, so 3. Neural Networks in Tacti- that relatively little time or effort would be involved cal Asset Location: A Comparative Study with in data collection. Furthermore, the time and effort Regression Models. London Business School, Depart- required to train a NN would be much less than ment of Decision Science, London, that required to extract and translate an expert's 4.

Lapedes A, Farber R. Nonlinear Signal Processing knowledge base for an expert system. MacKay C. Extraordinary Popular Delusions and the guarantee an optimal solution to a problem or cannot Madness of Crowds. Noonday Press, Rep. Although, century ed. O'Reilly B. Computers that think like people. Fortune February ; consistently good classifications, generalisations, or 7.

White H. Economic prediction using neural nets: The decisions, in a statistical sense. One of the major case of the IBM daily stock returns. Davidson C. Trained to think. Technology ; 7 3 9. Marose RA. A financial neural network application. Amongst the widely used neural network models, AI Expert May ; multilayer perceptrons with backpropagation neural Barker D.

Analysing financial health: Integrating neu- network model seem to be the most successful ral networks and expert systems. However, more research is required to Berry RH, Trigueiros D. Applying neural networks to the extraction of knowledge from accounting reports: determine if there are other types of problems that A classification study. In: Trippi, Turban eds. Neural may be good candidates. The backpropagation algor- Networks in Financing and Investing.

Probus Pub- ithm suffers some drawbacks, but the recent cascade- lishing, ; correlation networks [46] tend to resolve most of Klemic GG. The use of neural network computing these problems. Having said that, it is important to technology to develop profiles of Chapter 11 debtors who are likely to become tax.

In: Trippi, Turban note that this study did not cover all the NN models eds. Neural Networks in Financing and Investing. A neural network machines, and Hopfield networks. The methods Bankruptcy prediction by neural network.

In: Trippi, are statistical techniques, traditional ratio analysis, Turban eds. Neural Networks in Financing and econometric models i. Probus Publishing, ; hypothesis , Portfolio management theory i. Koutsougeras C, Papachristou G. These methods have been used for a number Therefore, at each iteration, the performance function is always reduced. The LM algorithm merges the best attributes of the steepest-descent algorithm and the Gauss-Newton technique.

Also, many of their limitations avoided. Theory of scaled conjugate gradient In the backpropagation algorithm, the weights are adjusted in the steepest descent direction negative of the gradient because the performance function decreases rapidly in this direction. But, the rapid reduction of performance function in this direction does not imply the fastest convergence always. The search is done along the conjugate directions in the conjugate gradient algorithms thus, generally producing speedier convergence than the steepest-descent direction.

To find the length of the updated weight step size, most of the algorithms use a learning rate. But, the step size is modified in each iteration in most of the conjugate gradient algorithms. Therefore, to reduce the performance function, the search is done along the conjugate gradient direction to find the step size.

A key advantage of the scaled conjugate gradient is that it does not line search at each iteration as compared to all other conjugate gradient algorithms. In the line search, the network responses of all training inputs are computed some times for every search which is computationally expensive. Thus, to avoid time-consuming line searches the scaled conjugate gradient algorithm SCG was designed by Moller, It does not include any critical user-dependent parameters, and it is much faster than the Levenberg-Marquardt Backpropagation.

We can use this algorithm on any dataset, if net input, weight and transfer functions of the given dataset have a derivative function. Derivatives of performance concerning bias variable X and weight are calculated using backpropagation. So, it avoids line search at every iteration to approximate scale step size by using the LM algorithm Hagan et al. Training phase stops when any of the following conditions appear: If the maximum number of repetitions achieved.

If maximum time is overshot. The performance reduced to the target. If the gradient of the performance is lower than the minimum gradient. If the validation performance has crossed the maximum fail times since the last time it decreased when using validation.

Theory of Bayesian regularization Bayesian regularized artificial neural networks BRANNs eliminate or reduce the requirement for lengthy cross-validation. Hence perform more robustly than standard backpropagation. A key advantage of this algorithm is that it considers the probabilistic nature of the weights in the network related to the given data set.

The probability of overfitting increases dramatically as more hidden layer of neurons added in the neural network. Thus for a stopping point, it requires a validation set. In this algorithm all unreasonable complex models penalized by pushing extra linkage weights to zero. The network will train and calculate the non-trivial weights. As the network grows some parameters will converge to a constant. Also, the volatility and noise in stock markets lead to the probability of overtraining for basic backpropagation networks.

But, Bayesian networks are more parsimonious and tend to reduce the probability of overfitting and eliminate the need for a validation step. Therefore, the available data for training is increased Jonathon, Bayesian regularization has the same usage criteria as the Scale Conjugate Gradient Backpropagation algorithm. This algorithm minimizes the weights and a linear combination of squared errors. For good generalization qualities of the network, this algorithm modifies the linear combinations Guresen et al.

This Bayesian regularization takes place within the Levenberg-Marquardt algorithm. Results Performance plots The performance plots help us to identify the number of iterations epochs at which the mean squared error become least or stops changing. The number of iterations does not represent time as we can see that Scaled Conjugate Gradient gives the best validation in 54 iterations and Levenberg-Marquardt gives in 10 13 iterations on tick dataset min dataset but the time taken by Scaled Conjugate Gradient is less than Levenberg-Marquardt in both datasets.

From Fig. We see that Bayesian Regularization is giving least mean squared error compared to Levenberg-Marquardt, followed by Scaled Conjugate Gradient when overall performance over all datasets. But, when only the performance on test dataset is compared, the Scaled Conjugate Gradient gives the best performance.

The testing dataset is chosen at random from the dataset. Regression plots The network performance is validated through regression plots. Thus, the network output regarding targets for training, validation, testing, and overall datasets are displayed by the regression plots. The Bayesian Regularization uses the whole validation dataset for training as well. We can see that the fit is very good for all tick data sets as the R values in each case of 0. Only Bayesian Regularization gives R-value of almost 0.

But, when only the regression plots on test dataset are compared, the Scaled Conjugate Gradient gives best results. These plots portray that prediction over tick dataset gives better predictions than prediction over the min dataset. We can see that there is not significant change in accuracy for all the algorithms.

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Neural Networks: Finance Prediction

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