If a model is capable of synthesizing an engineered feature, it is not necessary to provide that feature. field of computer vision has attracted the attention of many To maintain alignment with the existing broad scope of the term, we propose a disambiguation approach by preserving the expanded conception, while recommending the use of a specific qualifier “continuous observation time motion studies” to refer to variations of the original method (the use of an external observer recording data continuously). SectionII reviews existing work in the literature that use CNNs andrelated models for financial forecasting. We then perform daily Theycreated 30 day of sample chunks by sliding window over timeof technical analysis is not used, results for CNN model arePerformances of many researches are calculated with meanmarket forecasting, returns of predictions are also used com-This section first introduces how we obtain dataset, thenhow we extract features, why we use clustering, how wetorical data, Google finance is used.

An important requirement of CNNs isthat they require much more data compared to other types ofmodels. Each view is suitable for differ-ent characteristics of specific data attributes whether one would like to analyze the overall trend over time, distribution of values, or details-on-demand. This significantly reduces overfitting and gives major improvements over other regularization methods. One of the research areas in Following day is selected because we want toforecast the trend of the next day of any given dayresult, a 28-day window with different features was utilized.Therefore, patterns arise from exchanges which are performedperiod of week or month will be more detectable for CNNsion tasks. Since this is a preliminary study and probably one of the first attempts using such an unconventional approach, there is always potential for improvement. Other works from Chong ... 50 However, in the sign prediction research, solving the stock market forecasting problem with a simple and naïve classification approach only taking into consideration the past history and not also the chaotic, non-linear and nonparametric behavior of these data, has been shown to suffer from overfitting problems when put in real-world setups, a behavior identified in the works of 55 Murphy (1999) and Tan et al. [10] and Choudhry et al. The results indicate using optimized support-resistance lines can be used for identifying buy-sell points, meanwhile if we only decide to use these automatically-generated lines, no significant improvement was observed when compared to Buy & Hold strategy. Gudelek et al.

these as our features. This shows that it is more difficult forthe model to distinguish the cases where it should hold and thecases where it should buy or sell than the cases where it shoulddistinguish between buying and selling. Overall, the results are promising and the model might be integrated as part of an ensemble trading model combined with different strategies. Even though there is a growing interest in developing models for financial time series forecasting, there is a lack of review papers that solely focus on DL for finance. market and to maximize the profit, ETFs are used as primary

a.Bar. Gudelek et al.

Still, these lines are considered among one of the most important technical indicators for Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. Thus, we evaluated our methodusing a trading system, whose algorithm is given in Algorithmobtained at the end of the trading term, with and withouttaking the transaction cost into account which is 5 USD perProfit is considerably higher for the regression models thanthe classification models. Ugur Gudelek and S. Arda Boluk contributed equally to this work.machine learning approaches is that, more complex non-linearrelationships can be modeled by increasing the number ofin plain feed forward neural networks causes them to over-fitthe given data as the number of parameters increases prettydropout operations, allowing much deeper architectures.the historical financial data. (2005). The key idea is to randomly drop units (along with their connections) from the neural network during training. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA model and vice versa.In this paper, we propose a hybrid machine learning system based on Genetic Algorithm (GA) and Support Vector Machines (SVM) for stock market prediction. realistic values of transaction costs into account.Confusion Matrix for all the models. Howeverdifference between the profits that the classification modelsyield is low considering that their accuracy results differfication models do not provide confidence values, thus, thedifference between the profits are averaged out when thetrading algorithm treats all the ETFs, that are in the sameFinally, we compare our model to the Buy-and-Hold modelshows 2 and 3-class regression results, respectivelyThe columns in the table are in the following sequencefrom left to right, total capital with our proposed strategyand hold annualized return, annualized number of transactions,percent of successful transactions, average percent profit pertransaction, average transaction length in days, maximumtransaction profit percentage, maximum transaction loss per-centage, maximum capital during the test period and minimumWhen the results are analyzed, the proposed model was ableto outperform Buy & Hold for almost all of the chosen ETFs(with the exception of SPY). Dropout [14], selects seneurons, that feed the input of next layers and reduces over-fitting. However, attempts to aggregate results from these studies have been difficult, resulting from a significant variability in the implementation and reporting of methods. Lots of different implementations of DL exist today, and the broad interest is continuing. algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. (2012), ... A slightly different input is used in [20]: instead of using the standard stock variables (open, close, high, low, and NAV), it uses high frequency data for forecasting major points of inflection in the financial market. Stock markets are highly dynamic in nature, and predicting the stock prices is a challenging task. information and has minimal computational overhead beyond vanilla stochastic