{"id":16970,"date":"2022-08-11T03:11:36","date_gmt":"2022-08-10T19:11:36","guid":{"rendered":"https:\/\/www.tejwin.com\/?post_type=insight&#038;p=16970"},"modified":"2024-06-17T11:24:39","modified_gmt":"2024-06-17T03:24:39","slug":"predicting-the-occurrence-of-a-corporate-crisis-logit-probit","status":"publish","type":"insight","link":"https:\/\/www.tejwin.com\/en\/insight\/predicting-the-occurrence-of-a-corporate-crisis-logit-probit\/","title":{"rendered":"Predicting the occurrence of a corporate crisis Logit &amp; Probit"},"content":{"rendered":"\n<p id=\"8478\">Exploiting the Logit &amp; Probit regression model to analyze the chances of a company\u2019s bankruptcy.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter caption-align-center\"><img decoding=\"async\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/1_1eTt4AjMkfJhrcYjiTmHwwA.jpg\" alt=\"\"\/><figcaption class=\"wp-element-caption\">Photo by&nbsp;<a href=\"https:\/\/unsplash.com\/@goumbik?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText\" rel=\"noreferrer noopener\" target=\"_blank\">Lukas Blazek<\/a>&nbsp;on&nbsp;<a href=\"https:\/\/unsplash.com\/@goumbik?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText\" rel=\"noreferrer noopener\" target=\"_blank\">Unsplash<\/a><\/figcaption><\/figure>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_81 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69f145959f8f9\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"ez-toc-cssicon\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69f145959f8f9\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.tejwin.com\/en\/insight\/predicting-the-occurrence-of-a-corporate-crisis-logit-probit\/#Highlights\" >Highlights<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.tejwin.com\/en\/insight\/predicting-the-occurrence-of-a-corporate-crisis-logit-probit\/#Preface\" >Preface<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.tejwin.com\/en\/insight\/predicting-the-occurrence-of-a-corporate-crisis-logit-probit\/#The_Editing_Environment_and_Modules_Required\" >The Editing Environment and Modules Required<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.tejwin.com\/en\/insight\/predicting-the-occurrence-of-a-corporate-crisis-logit-probit\/#Database\" >Database<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.tejwin.com\/en\/insight\/predicting-the-occurrence-of-a-corporate-crisis-logit-probit\/#Conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.tejwin.com\/en\/insight\/predicting-the-occurrence-of-a-corporate-crisis-logit-probit\/#Full_code\" >Full code<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.tejwin.com\/en\/insight\/predicting-the-occurrence-of-a-corporate-crisis-logit-probit\/#See_More\" >See More<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.tejwin.com\/en\/insight\/predicting-the-occurrence-of-a-corporate-crisis-logit-probit\/#About_Us\" >About Us<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\" id=\"5f0e\"><span class=\"ez-toc-section\" id=\"Highlights\"><\/span><strong>Highlights<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Difficulty\uff1a\u2605\u2605\u2605\u2606\u2606<\/li>\n\n\n\n<li>Different explanatory variables are added to the regression model to discuss the probability of crisis in Taiwanese companies<\/li>\n\n\n\n<li>Advice: In terms of models, this article will not delve into the composition of the regression architecture, but will only analyze the results of the model and examples, so we need to have a basic concept of the statistical model, read&nbsp;<a href=\"https:\/\/medium.com\/tej-api-%E9%87%91%E8%9E%8D%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90\/%E9%87%8F%E5%8C%96%E5%88%86%E6%9E%90-%E4%B8%89-%E9%A0%90%E6%B8%AC%E5%B8%82%E5%A0%B4-bde88352a011\" target=\"_blank\" rel=\"noopener\">[Quantitative Analysis (3)] Predict the market?!<\/a>&nbsp;<strong><em>In addition, the subject matter mentioned herein is for illustrative purposes only and does not represent any financial product recommendation or recommendation.<\/em><\/strong><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"b9d7\"><span class=\"ez-toc-section\" id=\"Preface\"><\/span><strong>Preface<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p id=\"8d64\">Predicting the future is what every investor wants to pursue, whether it is for the future market or the future of the companies and the industries. Still, there is always uncertainty and randomness in predicting the future, so we can only use the past historical data and existing company indicators to put it into statistics for verification. This article and the previous article&nbsp;<a href=\"https:\/\/medium.com\/tej-api-%E9%87%91%E8%9E%8D%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90\/%E9%87%8F%E5%8C%96%E5%88%86%E6%9E%90-%E4%B8%89-%E9%A0%90%E6%B8%AC%E5%B8%82%E5%A0%B4-bde88352a011\" target=\"_blank\" rel=\"noopener\">[quantitative analysis (3)] predict the market?!<\/a>&nbsp;The content might be similar. Still, the difference is that this article predicts the future of the companies and compares them with the three statistical methods.<\/p>\n\n\n\n<p id=\"57ad\">Using different indicators, ROA, CR, DR, ATTNVR, CFAT, RSize, Sigma, ExRET, the verification found that some indicators had explanatory and incorporated regression models to explain the probability of crises.<\/p>\n\n\n\n<p id=\"196d\">The regression variables in this article refer to the relevant papers of the two authors<\/p>\n\n\n\n<p id=\"7b3c\">1. Altman z-score model (1968)<br>2. Shumway (2001)<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"d5ef\"><span class=\"ez-toc-section\" id=\"The_Editing_Environment_and_Modules_Required\"><\/span><strong>The Editing Environment and Modules Required<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p id=\"344c\">This article uses Spyder as the editor<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">###Three Treasures of Quantify\u3001Kit tools<br>import pandas as pd<br>import numpy as np<br>import tejapi<br>import statsmodels.formula.api as smf<br>import datetime as dt<br>from dateutil.relativedelta import *tejapi.ApiConfig.api_key = \"YOUR_KEY\"<br>tejapi.ApiConfig.ignoretz = True <br>###Ignores time zone in the time field<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"726e\"><span class=\"ez-toc-section\" id=\"Database\"><\/span><strong>Database<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p id=\"bbf7\"><em>Company basic information details column description:<\/em><\/p>\n\n\n\n<p id=\"98ae\"><em>Targets:&nbsp;<\/em>listed company<em>,&nbsp;<\/em>over-the-counter (OTC) company<em>,<\/em>&nbsp;emerging stock company<em>,<\/em>&nbsp;public company<\/p>\n\n\n\n<p id=\"4741\"><em>The database code is (TWN\/AIND), and its column is \u201cCrisis Day, Crisis Event Category.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n<pre class=\"wp-block-preformatted\">df1 <strong>=<\/strong> tejapi<strong>.<\/strong>get('TWN\/AIND', <br>                chinese_column_name <strong>=<\/strong> <strong>True<\/strong>,<br>                paginate <strong>=<\/strong> <strong>True<\/strong>,<br>                opts<strong>=<\/strong>{'columns':['coid','mdate', 'dflt_d','fail_fg']})<br><em>###use TEJ API to retrieve the required information<\/em><\/pre>\n\n\n\n<p id=\"654e\">First of all, the obtained information is classified. The pre-listing companies are excluded since we measure the \u201cprobability of crisis event occurrence.\u201d Hence, we define&nbsp;<strong>the crisis event<\/strong>&nbsp;situation as 1, and start processing the data. We should reset the event date to facilitate the subsequent merger with the financial report information.<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">df2 <strong>=<\/strong> df1[df1['\u5371\u6a5f\u4e8b\u4ef6\u985e\u5225'] <strong>!=<\/strong> ''] <br><em>#<\/em>Filter out those with default eventsdf2['\u5e74\/\u6708'] <strong>=<\/strong> df2[['\u5371\u6a5f\u767c\u751f\u65e5']]<strong>.<\/strong>applymap(<strong>lambda<\/strong> x: x<strong>.<\/strong>strftime('%Y-%m'))<strong>.<\/strong>astype('datetime64') <br><em>#<\/em>Change the date to the beginning of the month.df2['\u6708'] <strong>=<\/strong> df2['\u5e74\/\u6708']<strong>.<\/strong>dt<strong>.<\/strong>month <br><em>#t<\/em>ake out the monthdf2<strong>.<\/strong>reset_index(inplace <strong>=<\/strong> <strong>True<\/strong>)<strong>for<\/strong> i <strong>in<\/strong> range(len(df2<strong>.<\/strong>index)): <br>    <strong>if<\/strong> df2['\u6708'][i] <strong>==<\/strong> 1 <strong>or<\/strong> df2['\u6708'][i] <strong>==<\/strong> 4 <strong>or<\/strong> df2['\u6708'][i] <strong>==<\/strong> 7 <strong>or<\/strong> df2['\u6708'][i] <strong>==<\/strong> 10:<br>        df2['\u5e74\/\u6708'][i] <strong>=<\/strong> df2['\u5e74\/\u6708'][i]<strong>+<\/strong> relativedelta(months <strong>=<\/strong> <strong>+<\/strong>2)<br>    <strong>if<\/strong> df2['\u6708'][i] <strong>==<\/strong> 2 <strong>or<\/strong> df2['\u6708'][i] <strong>==<\/strong> 5 <strong>or<\/strong> df2['\u6708'][i] <strong>==<\/strong> 8 <strong>or<\/strong> df2['\u6708'][i] <strong>==<\/strong> 11:<br>        df2['\u5e74\/\u6708'][i] <strong>=<\/strong> df2['\u5e74\/\u6708'][i]<strong>+<\/strong> relativedelta(months <strong>=<\/strong> <strong>+<\/strong>1)<em>#<\/em>In order to match the financial report information later, we need to deal with the date first.df2['Y'] <strong>=<\/strong> 1 <em>#<\/em>Set all crisis event categories to 1<em><br><\/em>df2<strong>.<\/strong>rename(columns<strong>=<\/strong> {'\u516c\u53f8\u7c21\u7a31':'\u516c\u53f8'}, inplace<strong>=True<\/strong>)<\/pre>\n\n\n\n<p id=\"490d\">Then fish out the open-ended data of the whole market ledger account and select the required variables (ledger accounts), including:<\/p>\n\n\n\n<p id=\"9619\">X1 = working capital(R678)\/total asset (0010)<\/p>\n\n\n\n<p id=\"f7cd\">X2 = retained earnings (2341)\/ total asset<\/p>\n\n\n\n<p id=\"ea80\">X3 = EBIT (2402)\/total asset<\/p>\n\n\n\n<p id=\"65c2\">X4 = market value (MV)\/ total debt (1000)<\/p>\n\n\n\n<p id=\"e5e1\">X5 = revenue\/ total asset (R607)<\/p>\n\n\n\n<p id=\"92ce\">X6 = ROA (R11V)<\/p>\n\n\n\n<p id=\"c5d0\">X7 = debt ratio (R505)<\/p>\n\n\n\n<p id=\"9239\"><a href=\"https:\/\/www.tejwin.com\" target=\"_blank\" aria-label=\" (opens in a new tab)\" rel=\"noreferrer noopener\" class=\"ek-link\">TEJ ledger account details and codes<\/a><\/p>\n\n\n\n<p id=\"2403\">Since the ratio of data retrieved at a time is limited (&nbsp;<strong>paginate = True, up to 1 million fetches per salvage<\/strong>&nbsp;), we need to fish in segments and merge them into the same Dataframe.<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">###Each Dataframe has about 300,000 data and so on.a1 <strong>=<\/strong> tejapi<strong>.<\/strong>get('TWN\/AIFIN', <em>#\u5f9eTEJ api\u6488\u53d6\u6240\u9700\u8981\u7684\u8cc7\u6599<\/em><br>                chinese_column_name <strong>=<\/strong> <strong>True<\/strong>,<br>                paginate <strong>=<\/strong> <strong>True<\/strong>,<br>                mdate <strong>=<\/strong> {'gt':'2008-01-01', 'lt':'2011-01-01'},<br>                acc_code <strong>=<\/strong> ['R678', '0010','2341','2402','MV','1000', 'R607','R11V', 'R505'])a2 = tejapi.get('TWN\/AIFIN', <br>                chinese_column_name = True,<br>                paginate = True,<br>                mdate = {'gt':'2011-01-01', 'lt':'2014-01-01'},<br>                acc_code = ['R678', '0010','2341','2402','MV','1000', 'R607','R11V', 'R505'])a3 = tejapi.get('TWN\/AIFIN', <br>                chinese_column_name = True,<br>                paginate = True,<br>                mdate = {'gt':'2014-01-01', 'lt':'2017-01-01'},<br>                acc_code = ['R678', '0010','2341','2402','MV','1000', 'R607','R11V', 'R505'])a4 = tejapi.get('TWN\/AIFIN', <br>                chinese_column_name = True,<br>                paginate = True,<br>                mdate = {'gt':'2017-01-01', 'lt':'2020-01-01'},<br>                acc_code = ['R678', '0010','2341','2402','MV','1000', 'R607','R11V', 'R505'])a5 = tejapi.get('TWN\/AIFIN', <br>                chinese_column_name = True,<br>                paginate = True,<br>                mdate = {'gt':'2020-01-01'},<br>                acc_code = ['R678', '0010','2341','2402','MV','1000', 'R607','R11V', 'R505'])###Consolidated data<br>acc = pd.concat([a1,a2,a3,a4,a5])<\/pre>\n\n\n\n<p id=\"ec6f\">Converting the account values and putting them into colons is conducive to calculating the ledger accounts into the required variables. The calculation logic comes from the references and sets the company with no crisis after the merger to 0, and there is still a special value to be deleted.<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">acc1 = acc.pivot_table(values='\u6578\u503c', index=['\u516c\u53f8','\u5e74\/\u6708'], columns='\u6703\u8a08\u79d1\u76ee')<br>acc1['X1'] = (acc1['R678']\/acc1['0010'])*100<br>acc1['X2'] = (acc1['2341']\/acc1['0010'])*100<br>acc1['X3'] = (acc1['2402']\/acc1['0010'])*100<br>acc1['X4'] = (acc1['MV']\/acc1['1000'])*100<br>acc1 = acc1.rename(columns = {'R607':'X5', 'R11V':'X6', 'R505':'X7'})<br>acc2 = acc1[['X1','X2','X3','X4','X5','X6','X7']]acc2<strong>.<\/strong>reset_index(inplace<strong>=True<\/strong>)<br>df3 <strong>=<\/strong> pd<strong>.<\/strong>merge(acc2, df2[['\u516c\u53f8','\u5e74\/\u6708','Y']], how<strong>=<\/strong>'outer')<br>df3['Y'] <strong>=<\/strong> df3['Y']<strong>.<\/strong>replace(np<strong>.<\/strong>nan, 0) <br><em>#<\/em>Set no crisis to 0df3 <strong>=<\/strong> df3<strong>.<\/strong>dropna()<br>df3['X4'] <strong>=<\/strong> df3['X4']<strong>.<\/strong>drop([59690,59688]) <br><em>#<\/em>remove the infinite valuedf3 = df3.rename(columns = {'\u72c0\u6cc1':'Y'})<\/pre>\n\n\n\n<p id=\"882e\">Then bring in the variables to test their explanatory forces in three models, and we can find:<\/p>\n\n\n\n<p id=\"d7ce\">The variables X1 are not significant in the Logit model, while X2, 5, and 6 are significant in all three models, and the predicted results are intuitively consistent with us. Taking ROA as an example, the higher the ROA, the better the company\u2019s ability to use assets to profit, and the lower the probability of failure, which is negative and in line with expectations.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/1_1H3NSq7DOcPspxPaGnmMvjg.png\" alt=\"\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/1o11qXR1CgVqILP3GqPLHjg.png\" alt=\"\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/1qB9Cp_V41rrzKfxu1775lA.png\" alt=\"\"\/><\/figure>\n\n\n\n<p id=\"3924\">Then bringing the rapid performance data of individual companies into the regression model to obtain the probability of the company\u2019s crisis, we listed 2330 TSMC, 2454 MediaTek, the next market 1592 Enterex KY, 2475 CPT.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/1YAkqNsvH2f4l7rEr-85COQ.png\" alt=\"\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/19nUGkXmAnQSkpXCiVMPtlw.png\" alt=\"\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/1xkz5zljqdYvGO_v469ymmQ.png\" alt=\"\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/1JGzJrOLUEHUS6VbeKY_HcQ.png\" alt=\"\"\/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"2940\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p id=\"bd2a\">We can find that the crisis event of 2330 TSMC and 2454 MediaTek are extremely low. Assuming that he can be compared to see the LPM model (OLS), the crisis probability of 1592 Enterex KY and 2475 CPT are at least three&nbsp;<strong>times<\/strong>&nbsp;more than that of 2330 TSMC and 2454 MediaTek. Intuitively speaking, 2330 TSMC and 2454 MediaTek get better market value size; their company\u2019s constitution must be better than other companies. This forecast does not represent absolute. We only use the relevant accounting data to show the state to make a regression verification but still have a certain reference and explanatory power, which also means there is uncertainty in the stock market. If there is a signal that seems to be a crisis, it should always be concerned and pay attention to the assessment of its own risk; therefore, welcome to continue to pay attention to this platform. We will have more articles to share with you later. In addition, all the readers and investors are welcome to buy the solution in&nbsp;<a href=\"https:\/\/eshop.tej.com.tw\/E-Shop\/index\" rel=\"noreferrer noopener\" target=\"_blank\">the TEJ E Shop<\/a>. Try to see the holdings in their hands and the possibility and comparison of the occurrence of the crisis. I believe the reader has a complete database so that you can grasp the stock market\u2019s uncertainty.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"5890\"><span class=\"ez-toc-section\" id=\"Full_code\"><\/span><strong>Full code<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/gist.github.com\/tej87681088\/f991706a7c08f5c3fa8742a4e6b4df37#file-tejapi-medium-15-ipynb\" class=\"ek-link\" target=\"_blank\" rel=\"noopener\">Click here to go Github<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"d4f6\"><span class=\"ez-toc-section\" id=\"See_More\"><\/span><strong>See More<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.tejwin.com\/en\/insight\/predict-the-market\/\" class=\"ek-link\">Predicting the market?!<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.tejwin.com\/en\/insight\/lasso-regression-model\/\" class=\"ek-link\">Lasso regression model<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"505e\"><span class=\"ez-toc-section\" id=\"About_Us\"><\/span><strong>About Us<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/api.tej.com.tw\/index.html\" rel=\"noreferrer noopener\" target=\"_blank\">TEJ API<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/eshop.tej.com.tw\/E-Shop\/Edata_intro\" rel=\"noreferrer noopener\" target=\"_blank\">TEJ E-Shop<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Exploiting the Logit &amp; Probit regression model to analyze the chances of a company\u2019s bankruptcy. Highlights Preface Predicting the future is what every investor wants to pursue, whether it is for the future market or the future of the companies and the industries. Still, there is always uncertainty and randomness in predicting the future, so [&hellip;]<\/p>\n","protected":false},"featured_media":16973,"template":"","tags":[2604,2371,3007],"insight-category":[690,3509,50],"class_list":["post-16970","insight","type-insight","status-publish","has-post-thumbnail","hentry","tag-machine-learning","tag-python","tag-tejapi-data-analysis","insight-category-data-analysis","insight-category-fintech-en","insight-category-fintech"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/16970","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight"}],"about":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/types\/insight"}],"version-history":[{"count":1,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/16970\/revisions"}],"predecessor-version":[{"id":24238,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/16970\/revisions\/24238"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media\/16973"}],"wp:attachment":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media?parent=16970"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/tags?post=16970"},{"taxonomy":"insight-category","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight-category?post=16970"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}