Robbie Culkin’s research while affiliated with Santa Clara University and other places

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Publications (2)

A simple neural network with two hidden layers of two nodes each, four inputs, and a single output node.
Depiction of the source data, prices and returns. The top panel shows the sparse data on prices and the lower panel shows the data converted to returns, using the formula: Rt=(St−St−1)/St, where R,S stand for returns and stock prices, respectively. One row of data is lost when converting prices to returns. Note that for the index, we convert the data into sign of movement, where the value is 1 if the index moved up, else it is 0. The “NA” values show days for which there is no data observation.
Depiction of the returns for each day in percentiles, where 500 stocks returns are reduced to 19 values for the following percentiles: 1, 2, 3, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 85, 90, 95, 97, 98, and 99. Note that for the index, the values remain as the sign of movement, where the value is 1 if the index moved up, else it is 0, just as shown in Figure 2.
Data for prediction analysis after rearrangement. The label is the sign of the S&P movement, shown in the last column. The feature set comprises L sets of C=19 percentiles for L lagged days.
The distribution of daily S&P 500 index returns from 1963–2016. The mean return is 0.00031 and the standard deviation is 0.01. The skewness and kurtosis are −0.62 and 20.68, respectively.

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Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction
  • Article
  • Full-text available

September 2018

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4,700 Reads

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31 Citations

Sanjiv R. Das

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Karthik Mokashi

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Robbie Culkin

We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index, but not strongly enough to reject market efficiency. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P 500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests.

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Figure 1. A simple neural network with two hidden layers of two nodes each, four inputs, and a single output node. 
Figure 2. The distribution of daily S&P 500 index returns from 1963-2016. The mean return is 0.00031 and the standard deviation is 0.01. The skewness and kurtosis are −0.62 and 20.68, respectively. 
Figure 3. Daily S&P 500 index returns from 1963-2016. 
Are Markets Truly Efficient? Experiments using Deep Learning for Market Movement Prediction

May 2018

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48 Reads

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3 Citations

Sanjiv Das

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Karthik Mokashi

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Robbie Culkin

We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests.

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Citations (2)

... This includes NN which can be broadly categorized into; recurrent neural networks (RNN), deep neural networks (DNN), and convolution neural networks (CNN). Among RNN and DNN, long-short term memory (LSTM) and multilayer perceptron (MLP) are very popular and show good accuracy [3][4][5][6][7][8]. On the other hand, CNN is not frequently used, due to dimensional input structure, complexity, cost, or response time but its effectiveness in extracting patterns as shown in a few studies is comparable to other NN architectures [9][10][11]. ...

Reference:

Profitability trend prediction in crypto financial markets using Fibonacci technical indicator and hybrid CNN model
Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction
Sanjiv R. Das

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Karthik Mokashi

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Robbie Culkin

... Findings by Konak and Seker (2014) confirm that the performance of the FTSE 100 index during the 2001-2009 period sustain the weak form of EMH. Furthermore, more recently Das, Mokashi, and Culkin (2018) examined the behavior of all stocks of the S&P 500 index for the period 1963-2016; applying deep learning algorithms (neural networks) to predict market returns these authors conclude that their tests, which use larger information sets tan previously used in weak-form tests of market efficiency, do not uncover strong evidence of market inefficiency. ...

Reference:

Testing the overreaction hypothesis in the mexican stock market
Are Markets Truly Efficient? Experiments using Deep Learning for Market Movement Prediction
Sanjiv Das

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Karthik Mokashi

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Robbie Culkin