Wang 2020a - Revision history

Rimni at 14:56, 18 February 2021

2021-02-18T14:56:49Z

Rimni: /* 2.3 Simulation experiments to predict stocks */

2021-02-18T14:55:03Z

‎2.3 Simulation experiments to predict stocks

Rimni at 14:53, 18 February 2021

2021-02-18T14:53:01Z

Scipediacontent at 07:58, 1 April 2020

2020-04-01T07:58:54Z

Scipediacontent at 07:55, 1 April 2020

2020-04-01T07:55:31Z

Scipediacontent at 14:40, 31 March 2020

2020-03-31T14:40:48Z

Scipediacontent at 14:39, 31 March 2020

2020-03-31T14:39:00Z

Rimni at 10:20, 30 March 2020

2020-03-30T10:20:20Z

Rimni at 10:18, 30 March 2020

2020-03-30T10:18:34Z

Rimni at 14:29, 27 March 2020

2020-03-27T14:29:08Z

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 ==4. Simulation results and analysis==
-The proposed portfolio theoretical model is verified and simulated by MATLAB software. Now we are ready to invest in 8 stocks, just select the top 8 from the stock ranking table 1 given in the previous section, which are recorded as <math>P_1 , P_2,\cdots, P_8</math> respectively. The simulation results are shown in Figure 3 and Figure 4.
+The proposed portfolio theoretical model is verified and simulated by MATLAB software. Now we are ready to invest in 8 stocks, just select the top 8 from the stock ranking table 1 given in the previous section, which are recorded as <math>P_1 , P_2,\cdots, P_8</math> respectively. The simulation results are shown in Figures [[#img-3|3]] and [[#img-4|4]].
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-Here, we need to focus on Figure 3. With this chart, we can easily see the distribution curve of risk and return. This will provide us with a basis for deciding which set of portfolios to choose. When we choose a point on the curve, we get a set of investment weights. If you are an investor who seeks high returns without fear of high risks, you can choose the top set of portfolios. Of course, most people will choose a relatively compromise solution, that is, the benefits are greater, but the risks can be tolerated.
+Here, we need to focus on [[#img-3|Figure 3]]. With this chart, we can easily see the distribution curve of risk and return. This will provide us with a basis for deciding which set of portfolios to choose. When we choose a point on the curve, we get a set of investment weights. If you are an investor who seeks high returns without fear of high risks, you can choose the top set of portfolios. Of course, most people will choose a relatively compromise solution, that is, the benefits are greater, but the risks can be tolerated.
-Figure 4 is an investment weight allocation chart for different risk appetites. When we choose an abscissa, it corresponds to a portfolio. Of course, we can also directly calculate the specific weight distribution data from the model. But in the form of a graph, it is more intuitive to see the difference in portfolio schemes under different risk preferences. The specific manifestation is that the investment ratio of each stock is different. When you choose a preference, you can directly get the specific investment allocation plan.
+[[#img-4|Figure 4]] is an investment weight allocation chart for different risk appetites. When we choose an abscissa, it corresponds to a portfolio. Of course, we can also directly calculate the specific weight distribution data from the model. But in the form of a graph, it is more intuitive to see the difference in portfolio schemes under different risk preferences. The specific manifestation is that the investment ratio of each stock is different. When you choose a preference, you can directly get the specific investment allocation plan.
 ==5. Conclusion==

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 ===2.2 Principle of BP neural network===
-A typical BP neural network includes an input layer, one or more hidden layers, and an output layer. Its network structure is shown in Figure 1. The algorithm learning process of BP neural network is mainly composed of input forward propagation and error back propagation. In the forward propagation process, input samples are input from the input layer, processed by the hidden layer units, and the actual output value of each unit is calculated according to the weight and threshold. If the actual output value and the expected value reach a predetermined error range at this time, the learning process ends successfully. The back-propagation method is to adjust the weight through the network error in the back, and modify the weight matrix according to the actual output and the expected output to reduce the error of the neural network structure [14,15].
+A typical BP neural network includes an input layer, one or more hidden layers, and an output layer. Its network structure is shown in [[#img-1|Figure 1]]. The algorithm learning process of BP neural network is mainly composed of input forward propagation and error back propagation. In the forward propagation process, input samples are input from the input layer, processed by the hidden layer units, and the actual output value of each unit is calculated according to the weight and threshold. If the actual output value and the expected value reach a predetermined error range at this time, the learning process ends successfully. The back-propagation method is to adjust the weight through the network error in the back, and modify the weight matrix according to the actual output and the expected output to reduce the error of the neural network structure [14,15].
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-In this experiment, we also use historical data to evaluate the model, and the verification method is full set verification. Figure 2 shows the accuracy and error rate of the model classification. Obviously, the accuracy is significantly higher than the error rate. In finance, it is not easy to achieve 72% accuracy. So, as long as the number of transactions is enough, the probability of profit is very considerable.
+In this experiment, we also use historical data to evaluate the model, and the verification method is full set verification. [[#img-2|Figure 2]] shows the accuracy and error rate of the model classification. Obviously, the accuracy is significantly higher than the error rate. In finance, it is not easy to achieve 72% accuracy. So, as long as the number of transactions is enough, the probability of profit is very considerable.
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-|style="padding:10px;"| [[Image:Draft_Wang_861907375-image58.png|372px]]
+|style="padding:10px;"| [[Image:Wang_2020a_2053_Figura3.png|372px]]
 |- style="text-align: center; font-size: 75%;"
 | colspan="1" style="padding-bottom:10px;"| '''Figure 3.''' Effective frontier curve
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-|style="padding:10px;"| [[Image:Draft_Wang_861907375-image59.png|372px]]
+|style="padding:10px;"| [[Image:Wang_2020a_2972_Figura4.png|372px]]
 |- style="text-align: center; font-size: 75%;"
 | colspan="1" style="padding-bottom:10px;"| '''Figure 4.''' Distribution of investment weight

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-|style="padding:10px;"|  [[Image:Draft_Wang_861907375-image29.png|center|334px]]
+| style="padding:10px;"|  [[Image:Draft_Wang_861907375-image29.png|center|334px]]
-| style="padding:10px;"|  [[Image:Draft_Wang_861907375-image30.png|center|358px]]
+| style="padding:10px;"|  [[Image:Wang_2020a_3767_Figura2b.png|center|358px]]
 |- style="text-align: center; font-size: 75%;"
 | colspan="2" style="padding-bottom:10px;"| '''Figure 2'''. Evaluation results of the model

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 In this experiment, we also use historical data to evaluate the model, and the verification method is full set verification. Figure 2 shows the accuracy and error rate of the model classification. Obviously, the accuracy is significantly higher than the error rate. In finance, it is not easy to achieve 72% accuracy. So, as long as the number of transactions is enough, the probability of profit is very considerable.
-{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: 85%;"
+{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: auto;max-width: auto;"
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 |style="padding:10px;"|  [[Image:Draft_Wang_861907375-image29.png|334px]]

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 [11] Svefors P., Sysoev O., Ekstrom E.C., et al. Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort Bangladesh. BMJ Open, 9(8):e025154, 2019.
-[12] Arabameri A., Pradhan B., Rezaei K. Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models. Geosciences Journal, 1:1-18, 2019.
+[12] Arabameri A., Pradhan B., Rezaei K. Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models. Geosciences Journal, 23:669–686, 2019.
 [13] Dong Y., Wang H. Robust output feedback stabilization for uncertain discrete-time stochastic neural networks with time-varying delay. Neural Processing Letters, 51:83–103, 2020.

@@ Line 484: / Line 484: @@
 [6] Serdar M.A., Serteser M., Ucal Y., etc. An assessment of HbA1c in diabetes mellitus and pre-diabetes diagnosis: a multi-centered data mining study. Applied Biochemistry and Biotechnology, 190(Suppl1):1-13, 2019.
-[7] Sorensen E H. Miller K L, Ooi C K. The decision tree approach to stock selection-An evolving tree model performs the best. Journal of Portfolio Management. 2000,27(1):42-52.
+[7] Sorensen E.H. Miller K.L., Ooi C.K. The decision tree approach to stock selection-An evolving tree model performs the best. Journal of Portfolio Management, 27(1):42-52, 2000.
-[8] Piotroski, Joseph D . Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. Journal of Accounting Research, 2001, 38(2):43-51.
+[8] Piotroski J.D. Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38(2):43-51, 2001.
-[9] Fama E F , French K R . A Five-factor Asset Pricing Model. Journal of Financial Economics, 2015,116(1):1-22.
+[9] Fama E.F., French K.R. A five-factor asset pricing model. Journal of Financial Economics, 116(1):1-22, 2015.
-[10] Jigar Patel，Sahil Shah，Priyank Thakkar，K Kotecha. Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning technique[J]. Expert Systems with Applications．2015,42(1)：259-268.
+[10] Patel J., Shah S., Thakkar P., Kotecha K. Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning technique. Expert Systems with Applications, 42(1)：259-268, 2015.
-[11] Pernilla Svefors, Oleg Sysoev, Eva-Charlotte Ekstrom,etc. Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort, Bangladesh. BMJ Open, 2019, 9(8):e025154.
+[11] Svefors P., Sysoev O., Ekstrom E.C., et al. Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort Bangladesh. BMJ Open, 9(8):e025154, 2019.
-[12] Alireza Arabameri, Biswajeet Pradhan, Khalil Rezaei. Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models. Geosciences Journal, 2019, 1:1-18.
+[12] Arabameri A., Pradhan B., Rezaei K. Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models. Geosciences Journal, 1:1-18, 2019.
-[13] Yali Dong, Huimin Wang. Robust Output Feedback Stabilization for Uncertain Discrete-Time Stochastic Neural Networks with Time-Varying Delay. Neural Processing Letters, 2019:1-21.
+[13] Dong Y., Wang H. Robust output feedback stabilization for uncertain discrete-time stochastic neural networks with time-varying delay. Neural Processing Letters, 51:83–103, 2020.
-[14] Meng-Xiao Li, Su-Qin Yu, Wei Zhang. Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images. International Journal of Ophthalmology, 2019, 12(6):1012-1020.
+[14] Li M.X., Yu S.Q., Zhang W. Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images. International Journal of Ophthalmology, 12(6):1012-1020, 2019.
-[15] Marwin H. S. Segler, Mike Preuss, Mark P. Waller. Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 2018, 555(7698):604-610.
+[15] Segler M.H.S., Preuss M., Waller M.P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555(7698):604-610, 2018.
 </div>

@@ Line 476: / Line 476: @@
 [2] Chava S., Wang R., Zou H. Covenants, creditors’ simultaneous equity holdings, and firm investment policies. Journal of Financial and Quantitative Analysis, 54(2):481-512, 2019.
-GUO Han-ding ZHANG Yin-xian WU Si-cai SHANG Ling
+[3] Guo H., Zhang Y., Wu S., Shang L. Investment risk evaluation of existing building energy-saving renovation project for ESCO. Ecological Economy, 27(3):180-189, 2018.
-[3] Guo H., Zhang Y., Wu S., Shang L. Investment risk evaluation of existing building energy-saving renovation project for ESCO[J]. Ecological Economy, 27(3):180-189, 2018.
+[5] Ferrando A., Preuss C. What finance for what investment? Survey-based evidence for European companies. Econ. Polit., 35:1015–1053, 2018.
-[4] Huiqi Gan. Does CEO managerial ability matter? Evidence from corporate investment efficiency[J]. Review of Quantitative Finance & Accounting, 2019, 52(4):1085-1118.
+[6] Serdar M.A., Serteser M., Ucal Y., etc. An assessment of HbA1c in diabetes mellitus and pre-diabetes diagnosis: a multi-centered data mining study. Applied Biochemistry and Biotechnology, 190(Suppl1):1-13, 2019.
-[5] Ferrando, Annalisa, Preuss, Carsten. What finance for what investment? Survey-based evidence for European companies[J]. Eib Working Papers, 2018(5):1-39.
+[7] Sorensen E H. Miller K L, Ooi C K. The decision tree approach to stock selection-An evolving tree model performs the best. Journal of Portfolio Management. 2000,27(1):42-52.
-[6] Muhittin A. Serdar, Mustafa Serteser, Yasemin Ucal, etc. An Assessment of HbA1c in Diabetes Mellitus and Pre-diabetes Diagnosis: a Multi-centered Data Mining Study[J]. Applied Biochemistry and Biotechnology, 2019(Suppl1):1-13.
+[8] Piotroski, Joseph D . Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. Journal of Accounting Research, 2001, 38(2):43-51.
-[7] Sorensen E H. Miller K L, Ooi C K. The decision tree approach to stock selection-An evolving tree model performs the best[J]. Journal of Portfolio Management. 2000,27(1):42-52.
+[9] Fama E F , French K R . A Five-factor Asset Pricing Model. Journal of Financial Economics, 2015,116(1):1-22.
-−
-[8] Piotroski, Joseph D . Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers[J]. Journal of Accounting Research, 2001, 38(2):43-51.
-−
-[9] Fama E F , French K R . A Five-factor Asset Pricing Model[J]. Journal of Financial Economics, 2015,116(1):1-22.
 [10] Jigar Patel，Sahil Shah，Priyank Thakkar，K Kotecha. Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning technique[J]. Expert Systems with Applications．2015,42(1)：259-268.
-[11] Pernilla Svefors, Oleg Sysoev, Eva-Charlotte Ekstrom,etc. Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort, Bangladesh[J]. BMJ Open, 2019, 9(8):e025154.
+[11] Pernilla Svefors, Oleg Sysoev, Eva-Charlotte Ekstrom,etc. Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort, Bangladesh. BMJ Open, 2019, 9(8):e025154.
-[12] Alireza Arabameri, Biswajeet Pradhan, Khalil Rezaei. Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models[J]. Geosciences Journal, 2019, 1:1-18.
+[12] Alireza Arabameri, Biswajeet Pradhan, Khalil Rezaei. Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models. Geosciences Journal, 2019, 1:1-18.
-[13] Yali Dong, Huimin Wang. Robust Output Feedback Stabilization for Uncertain Discrete-Time Stochastic Neural Networks with Time-Varying Delay[J]. Neural Processing Letters, 2019:1-21.
+[13] Yali Dong, Huimin Wang. Robust Output Feedback Stabilization for Uncertain Discrete-Time Stochastic Neural Networks with Time-Varying Delay. Neural Processing Letters, 2019:1-21.
-[14] Meng-Xiao Li, Su-Qin Yu, Wei Zhang. Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images[J]. International Journal of Ophthalmology, 2019, 12(6):1012-1020.
+[14] Meng-Xiao Li, Su-Qin Yu, Wei Zhang. Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images. International Journal of Ophthalmology, 2019, 12(6):1012-1020.
-[15] Marwin H. S. Segler, Mike Preuss, Mark P. Waller. Planning chemical syntheses with deep neural networks and symbolic AI[J]. Nature, 2018, 555(7698):604-610.
+[15] Marwin H. S. Segler, Mike Preuss, Mark P. Waller. Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 2018, 555(7698):604-610.
 </div>