• Management Science and Financial Engineering
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• v.18 no.2
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• pp.1-4
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• 2012

We study optimal trading strategy of a market maker with stock inventory. Following Avellaneda and Stoikov (2008), we assume the stock price follows a normal distribution. However, we take a constant expected rate of the stock return and assume that the stock volatility is an inverse function of the stock price level. We show that the optimal bid-ask spread of the market maker is wider for a higher expected rate of stock returns.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.210-218
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• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.731-741
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• 2016

It has become five years since comprehensive programming licenses were rendered in Korea. Allocating a lion's share of their air time on live news and news commentaries, those channels have established a unique live production system or a broadcasting system which is heavily live production-oriented, to be exact. The live commentaries are filled with a mixture of news flashes, conventional news commentaries, and debates. Those channels get their news and commentary programs made through subsidiaries' where production directors and studio staffs belong. They, being very sensitive about viewer rating, tend to be aggressive about reruns of highly rated programs and they do not even seem to care when the regular programs actually went out. This kind of reckless strategy to pursue a higher viewer rating could limit not only new programming attempts but also exposure diversity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.229-235
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• 2020

Investors aim to maximize the return rate for their own investment, utilizing various information as possible as they can access. However those investors, especially individual investors, have limitations of interpretation of the domain-specific information or even the acquisition of the information itself. Thus, individual investors tend to make decision affectively and frequently, which may cause a loss in returns. This study aims to analyze analysts' target price and to suggest the strategy that could maximize individual's return rate. Most previous literature revealed that the optimistic bias exists in the analysts' target price and it is also confirmed in this study. In this context, this study suggests the upper limit of target rate of returns and the optimal value named 'alpha(α)' which performs the adjustment of proposed target rate to maximize excess earning returns eventually. To achieve this goal, this study developed an optimization problem using linear programming. Specifically, when the analysts' proposed target rate exceeds 30%, it could be adjusted to the extent of 59% of its own target rate. As apply this strategy, the investors could achieve 1.2% of excess earning rate on average. The result of this study has significance in that the individual investors could utilize analysts' target price practically.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.118-128
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• 2017

Uncertain factors in finical markets make the prediction of future returns and risk of asset much difficult. In this paper, a model,assuming the admissible errors on expected returns and risks of assets, assisted in the multiperiod mean variance portfolio selection problem is built. The model considers transaction costs, upper bound on borrowing risk-free asset constraints, cardinality constraints and threshold constraints. Cardinality constraints limit the number of assets to be held in an efficient portfolio. At the same time, threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Because of these limitations, the proposed model is a mix integer dynamic optimization problem with path dependence. The forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, to evaluate the model, our result of a meaning example is compared to the terminal wealth under different constraints.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.2
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• pp.94-103
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• 2008

This study measured technology transfer efficiency for public institutes. The study made use of DEA being one of the non-parametric linear programming to evaluate technology transfer efficiency for public institutes and to measure technology efficiency, pure technical efficiency and scale efficiency. The measurement of the technology transfer efficiency for public institutes was as follows: The cause of the technology transfer inefficiency was affected by pure technical inefficiency more than by scale inefficiency. Public institutes' RTS(Return To Scale) value varied depending upon the features of the organizations than the features of the regions. Public research institutes' RTS value is more effective than universities' RTS value. We compared the RTS group with the RTS of Projected DMU groups. The RTS group had constant returns to scale effect while the RTS of the Projected DMU had increasing returns to scale effect. The technology transfer efficiency of public institutes varied depending upon the features of the organizations and regions : The technology transfer efficiency of public institutes were as follows : public research institutes at the metropolitan area, public research institutes at the local areas, universities at the metropolitan area and universities at the local areas. In other words, the technology transfer efficiency was affected by organizational characteristics more than by regional characteristics at the place where public institutes were located.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.