{"id":17035,"date":"2025-11-07T18:21:58","date_gmt":"2025-11-07T12:51:58","guid":{"rendered":"https:\/\/trending.niftytrader.in\/?p=17035"},"modified":"2025-11-07T18:25:02","modified_gmt":"2025-11-07T12:55:02","slug":"how-ai-and-tech-disruption-are-transforming-the-stock-market-what-investors-should-know","status":"publish","type":"post","link":"https:\/\/www.niftytrader.in\/markets\/how-ai-and-tech-disruption-are-transforming-the-stock-market-what-investors-should-know\/","title":{"rendered":"How AI and Tech Disruption Are Transforming the Stock Market \u2014 What Investors Should Know"},"content":{"rendered":"<p data-start=\"448\" data-end=\"541\">AI in Stock Trading: Maximilian Goehmann Warns of Hidden Risks Amid Market Transformation<\/p>\n<p data-start=\"543\" data-end=\"790\">As artificial intelligence (AI) takes over modern stock trading with unprecedented precision, experts warn that small data errors in automated systems could cause massive financial instability \u2014 echoing past crises like the 2010 Flash Crash.<\/p>\n<p data-start=\"792\" data-end=\"1261\">AI is rapidly transforming global financial markets. Automated trading systems, powered by sophisticated algorithms, now drive nearly 70 percent of global equity transactions. The speed, scale, and efficiency they bring are unmatched \u2014 but so are the risks. According to Maximilian Goehmann, a PhD researcher at the London School of Economics (LSE), even the smallest flaws in AI-driven models can trigger cascading failures with trillion-dollar consequences.<\/p>\n<h2 data-start=\"1268\" data-end=\"1329\">The 2010 Flash Crash: A Lesson in Algorithmic Fragility<\/h2>\n<p data-start=\"1331\" data-end=\"1681\">On May 6, 2010, nearly $1 trillion in market value vanished within minutes on Wall Street. The now-infamous \u201cFlash Crash\u201d saw the Dow Jones plunge nearly 1,000 points before rebounding within half an hour. The cause? Not a cyberattack or massive sell-off \u2014 but an accumulation of minor algorithmic feedback loops feeding off erroneous data.<\/p>\n<p data-start=\"1683\" data-end=\"1939\">\u201cThe issue was that there were a lot of algorithms with similar settings that were each triggering each other,\u201d explains Goehmann. \u201cOne feedback loop was triggering the next, creating a cascading failure that led to a sudden and huge drop in the market.\u201d<\/p>\n<p data-start=\"1941\" data-end=\"2144\">This event, Goehmann says, underscores a critical truth: market meltdowns in the AI era need not stem from catastrophic system failures \u2014 small, unchecked data inconsistencies can be just as dangerous.<\/p>\n<p data-start=\"1941\" data-end=\"2144\">Also Read : <a href=\"https:\/\/www.niftytrader.in\/content\/k-v-kamath-says-india-right-to-wait-on-ai-backs-strong-valuations-and-ipo-market\/\">K.V. Kamath Says India Right to Wait on AI; Backs Strong Valuations and IPO Market<\/a><\/p>\n<h2 data-start=\"2151\" data-end=\"2204\">The Rise of Automated Trading and AI in Finance<\/h2>\n<p data-start=\"2206\" data-end=\"2438\">Automated and high-frequency trading (HFT) now dominate global markets. AI models analyze massive datasets \u2014 from real-time price patterns to geopolitical news \u2014 and execute trades in milliseconds, far beyond human capability.<\/p>\n<p data-start=\"2440\" data-end=\"2637\">Estimates suggest 60\u201370% of trades in US equity markets are now algorithmically executed. While this enhances liquidity and efficiency, it also amplifies volatility when models react in sync.<\/p>\n<p data-start=\"2639\" data-end=\"2781\">\u201cIf these algorithms fail to adapt to real-time market conditions or process flawed data, the results can be disastrous,\u201d Goehmann cautions.<\/p>\n<h2 data-start=\"1304\" data-end=\"1339\">What\u2019s Changing for Investors?<\/h2>\n<p data-start=\"1340\" data-end=\"1442\">Here\u2019s a table summarizing how AI\/tech disruption is affecting the market\u2014and what it means for you:<\/p>\n<div class=\"_tableContainer_1rjym_1\">\n<div class=\"group _tableWrapper_1rjym_13 flex w-fit flex-col-reverse\" tabindex=\"-1\">\n<table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"1444\" data-end=\"2014\">\n<thead data-start=\"1444\" data-end=\"1540\">\n<tr data-start=\"1444\" data-end=\"1540\">\n<th data-start=\"1444\" data-end=\"1486\" data-col-size=\"sm\">Disruption Type<\/th>\n<th data-start=\"1486\" data-end=\"1540\" data-col-size=\"md\">Impact for Investors<\/th>\n<\/tr>\n<\/thead>\n<tbody data-start=\"1635\" data-end=\"2014\">\n<tr data-start=\"1635\" data-end=\"1728\">\n<td data-start=\"1635\" data-end=\"1676\" data-col-size=\"sm\">Algorithmic &amp; High-Frequency Trading<\/td>\n<td data-col-size=\"md\" data-start=\"1676\" data-end=\"1728\">Speed becomes critical; small errors can cascade<\/td>\n<\/tr>\n<tr data-start=\"1729\" data-end=\"1824\">\n<td data-start=\"1729\" data-end=\"1771\" data-col-size=\"sm\">Predictive Analytics &amp; NLP<\/td>\n<td data-col-size=\"md\" data-start=\"1771\" data-end=\"1824\">Companies leverage data to anticipate moves<\/td>\n<\/tr>\n<tr data-start=\"1825\" data-end=\"1918\">\n<td data-start=\"1825\" data-end=\"1866\" data-col-size=\"sm\">Automation of Workforce &amp; Services<\/td>\n<td data-col-size=\"md\" data-start=\"1866\" data-end=\"1918\">Business models shift, winners and losers emerge<\/td>\n<\/tr>\n<tr data-start=\"1919\" data-end=\"2014\">\n<td data-start=\"1919\" data-end=\"1961\" data-col-size=\"sm\">Valuation Material Changes<\/td>\n<td data-col-size=\"md\" data-start=\"1961\" data-end=\"2014\">Traditional metrics may no longer hold<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2 data-start=\"2133\" data-end=\"2175\">Where Opportunities Are for Investors<\/h2>\n<p data-start=\"2176\" data-end=\"2554\">When well-executed, AI can create compelling stock-market opportunities. Companies leading in AI infrastructure, data platforms or cloud systems are getting premium valuations. For example, firms like Palantir Technologies and Oracle Corporation have seen large valuation boosts based on their AI positioning.\u00a0<br data-start=\"2523\" data-end=\"2526\" \/>For investors, this means:<\/p>\n<ul data-start=\"2555\" data-end=\"2873\">\n<li data-start=\"2555\" data-end=\"2622\">\n<p data-start=\"2557\" data-end=\"2622\">Look for companies with a clear AI strategy, not just hype.<\/p>\n<\/li>\n<li data-start=\"2623\" data-end=\"2749\">\n<p data-start=\"2625\" data-end=\"2749\">Consider enablers (infrastructure, chips, platforms) as well as users (firms adopting AI to transform operations).<\/p>\n<\/li>\n<li data-start=\"2750\" data-end=\"2873\">\n<p data-start=\"2752\" data-end=\"2873\">Be aware that legacy businesses that don\u2019t adapt may face structural decline.<\/p>\n<\/li>\n<\/ul>\n<h2 data-start=\"2880\" data-end=\"2919\">What Risks Should Investors Watch?<\/h2>\n<p data-start=\"2920\" data-end=\"3030\">The excitement around AI is real\u2014but the risks are equally significant. Here are some of the chief concerns:<\/p>\n<ul data-start=\"3031\" data-end=\"3870\">\n<li data-start=\"3031\" data-end=\"3255\">\n<p data-start=\"3033\" data-end=\"3255\">Data-error &amp; algorithmic cascade risk: Small flaws in automated systems can lead to major market shifts\u2014think of events like the 2010 Flash Crash, where a data or algorithmic mis-step triggered dramatic market moves.<\/p>\n<\/li>\n<li data-start=\"3256\" data-end=\"3502\">\n<p data-start=\"3258\" data-end=\"3502\">Valuation excess &amp; bubble risk: The Bank of England and the International Monetary Fund have both flagged the possibility that the AI-led tech rally may resemble the dot-com bubble in underlying risk.<\/p>\n<\/li>\n<li data-start=\"3503\" data-end=\"3711\">\n<p data-start=\"3505\" data-end=\"3711\">Winners vs losers diverging sharply: Companies slow to adopt AI, or whose business model is threatened by automation, are already being penalised in the market.<\/p>\n<\/li>\n<li data-start=\"3712\" data-end=\"3870\">\n<p data-start=\"3714\" data-end=\"3870\">Regulatory and ethical uncertainty: The \u201cblack box\u201d nature of AI, data privacy issues and algorithmic fairness present unknowns for long-term investors.<\/p>\n<\/li>\n<\/ul>\n<h2 data-start=\"3877\" data-end=\"3918\">How Can You Position Your Portfolio?<\/h2>\n<p data-start=\"3919\" data-end=\"3962\">Here\u2019s a practical roadmap for investors:<\/p>\n<ol data-start=\"3963\" data-end=\"4780\">\n<li data-start=\"3963\" data-end=\"4133\">\n<p data-start=\"3966\" data-end=\"4133\">Identify the theme \u2013 AI is broad. Decide whether you\u2019re investing in infrastructure (chips, cloud), software\/AI applications, or companies using AI to transform.<\/p>\n<\/li>\n<li data-start=\"4134\" data-end=\"4274\">\n<p data-start=\"4137\" data-end=\"4274\">Select companies with strong fundamentals \u2013 Look beyond hype: check balance sheets, earnings growth potential, AI strategy clarity.<\/p>\n<\/li>\n<li data-start=\"4275\" data-end=\"4414\">\n<p data-start=\"4278\" data-end=\"4414\">Balance your exposure \u2013 Include some high-growth AI plays but hedge with more stable companies, since volatility risk is elevated.<\/p>\n<\/li>\n<li data-start=\"4415\" data-end=\"4540\">\n<p data-start=\"4418\" data-end=\"4540\">Stay alert to risk signals \u2013 Monitor valuation levels, data\/infrastructure vulnerabilities, regulatory developments.<\/p>\n<\/li>\n<li data-start=\"4541\" data-end=\"4780\">\n<p data-start=\"4544\" data-end=\"4780\">Use keywords in your research or content \u2013 For example: <em data-start=\"4604\" data-end=\"4635\">AI disruption in stock market<\/em>, <em data-start=\"4637\" data-end=\"4664\">tech disruption investing<\/em>, <em data-start=\"4666\" data-end=\"4696\">which stocks benefit from AI<\/em>, <em data-start=\"4698\" data-end=\"4730\">AI risks for investors in 2025<\/em>. These help frame your thinking and your content.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<h2 data-start=\"2788\" data-end=\"2842\">The Data Problem: Small Errors, Big Consequences<\/h2>\n<p data-start=\"2844\" data-end=\"3046\">Goehmann\u2019s research focuses on how \u201csmall but frequent\u201d data inaccuracies \u2014 such as missing timestamps, duplicated quotes, or corrupted price feeds \u2014 can silently accumulate and lead to systemic risk.<\/p>\n<p data-start=\"3048\" data-end=\"3255\">\u201cThere\u2019s a lot of focus on the AI itself, but not enough on the data it\u2019s trained on,\u201d he notes. \u201cThe machine might interpret the data differently than intended, and that\u2019s where errors begin to multiply.\u201d<\/p>\n<p data-start=\"3257\" data-end=\"3438\">He argues that the 2010 Flash Crash wasn\u2019t a one-off anomaly but a warning of how algorithmic systems, left unmonitored, could react unpredictably in high-stress environments.<\/p>\n<h2 data-start=\"3445\" data-end=\"3494\">Policymakers and the Challenge of Oversight<\/h2>\n<p data-start=\"3496\" data-end=\"3727\">In a written submission to the UK Treasury Committee\u2019s inquiry into AI in financial services, Goehmann advised against excessive regulation, advocating instead for data transparency and voluntary certification frameworks.<\/p>\n<p data-start=\"3729\" data-end=\"3922\">\u201cOver-regulation is not the key,\u201d he says. \u201cRather than imposing new rules, policymakers should encourage frameworks that promote real-time transparency and competition-driven data accuracy.\u201d<\/p>\n<p data-start=\"3924\" data-end=\"4158\">He recommends that data providers be incentivized to certify and benchmark their data quality publicly. Such certification would reward firms maintaining high data accuracy, creating a market-driven system of accountability.<\/p>\n<p data-start=\"4160\" data-end=\"4474\">Goehmann also suggests data stress testing, akin to financial stress tests conducted by central banks, to identify vulnerabilities before they cascade into crises. \u201cThe Bank of England could implement mandatory data stress tests to simulate anomalies like missing values or inconsistent timestamps,\u201d he says.<\/p>\n<h2 data-start=\"4481\" data-end=\"4537\">Beyond Regulation: Building Smarter, Safer Systems<\/h2>\n<p data-start=\"4539\" data-end=\"4681\">Rather than adding layers of bureaucracy, Goehmann believes in enhancing real-time oversight mechanisms for algorithmic trading systems.<\/p>\n<p data-start=\"4683\" data-end=\"4957\">He proposes that the Financial Conduct Authority (FCA) integrate data quality monitoring within its existing certification systems. This would ensure that both algorithmic systems and their human overseers maintain high standards of risk management and compliance.<\/p>\n<p data-start=\"4959\" data-end=\"5087\">\u201cA truly free market regulates itself \u2014 but we can install warning signs through transparency and competition,\u201d he emphasizes.<\/p>\n<h2 data-start=\"5094\" data-end=\"5151\">Broader Impact: AI\u2019s Dual Edge in Financial Markets<\/h2>\n<p data-start=\"5153\" data-end=\"5338\">AI is revolutionizing nearly every corner of the financial ecosystem \u2014 from predictive analytics and sentiment analysis to robo-advisory platforms and fraud detection.<\/p>\n<ul data-start=\"5340\" data-end=\"5889\">\n<li data-start=\"5340\" data-end=\"5484\">\n<p data-start=\"5342\" data-end=\"5484\">Algorithmic and High-Frequency Trading (HFT): AI executes trades in microseconds, optimizing returns but also amplifying systemic risks.<\/p>\n<\/li>\n<li data-start=\"5485\" data-end=\"5611\">\n<p data-start=\"5487\" data-end=\"5611\">Predictive Analytics: Machine learning models identify patterns in massive data streams, enhancing market forecasting.<\/p>\n<\/li>\n<li data-start=\"5612\" data-end=\"5739\">\n<p data-start=\"5614\" data-end=\"5739\">Sentiment Analysis: Natural language processing (NLP) tools analyze social media and news feeds to gauge investor mood.<\/p>\n<\/li>\n<li data-start=\"5740\" data-end=\"5889\">\n<p data-start=\"5742\" data-end=\"5889\">Risk and Fraud Detection: AI enhances real-time surveillance, identifying anomalies in transaction patterns that could indicate manipulation.<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"5891\" data-end=\"6078\">While these innovations have made markets more efficient, they\u2019ve also introduced new vulnerabilities \u2014 especially when multiple systems interact unpredictably during market stress.<\/p>\n<h2 data-start=\"6085\" data-end=\"6126\">Balancing Innovation with Stability<\/h2>\n<p data-start=\"6128\" data-end=\"6337\">Goehmann\u2019s research highlights a growing global concern: as AI and machine learning dominate trading, financial stability depends less on human judgment and more on data integrity and algorithmic safeguards.<\/p>\n<p data-start=\"6339\" data-end=\"6557\">\u201cThe focus should not just be on what AI can do, but on ensuring that what it does is based on accurate, reliable data,\u201d he says. \u201cOtherwise, a single minor error can ripple through the system at the speed of light.\u201d<\/p>\n<p data-start=\"6559\" data-end=\"6796\">As governments and regulators worldwide grapple with how to govern AI in finance, experts like Goehmann are pushing for a balance between innovation and oversight \u2014 one that protects markets without stifling technological progress.<\/p>\n<p data-start=\"6798\" data-end=\"6940\">\u201cAI is transforming finance,\u201d he concludes, \u201cbut as with all revolutions, we must ensure that speed does not come at the cost of stability.\u201d<\/p>\n<ul>\n<li><a href=\"https:\/\/www.niftytrader.in\/nifty50-contributors\">Nifty 50<\/a><\/li>\n<li><a href=\"https:\/\/www.niftytrader.in\/nifty-bank-contributors\">Bank Nifty<\/a><\/li>\n<li><a href=\"https:\/\/www.niftytrader.in\/stocks-price\/bse\">Sensex<\/a><\/li>\n<\/ul>\n<h2 data-start=\"275\" data-end=\"367\"><strong data-start=\"278\" data-end=\"367\">Frequently Asked Questions (FAQs) on AI and Technology Disruption in the Stock Market<\/strong><\/h2>\n<h3 data-start=\"374\" data-end=\"460\"><strong data-start=\"378\" data-end=\"458\">1. How is Artificial Intelligence (AI) transforming the modern stock market?<\/strong><\/h3>\n<p data-start=\"461\" data-end=\"941\">AI is transforming the stock market by introducing <strong data-start=\"512\" data-end=\"575\">data-driven precision, automation, and predictive analytics<\/strong>. Algorithms now analyze vast datasets, execute trades in milliseconds, and identify patterns invisible to human traders. This automation enhances efficiency and liquidity but also increases the risk of <strong data-start=\"778\" data-end=\"795\">flash crashes<\/strong> and <strong data-start=\"800\" data-end=\"822\">algorithmic errors<\/strong> when data quality or logic fails. In essence, AI has made markets faster, smarter\u2014and occasionally more unpredictable.<\/p>\n<h3 data-start=\"948\" data-end=\"1035\"><strong data-start=\"952\" data-end=\"1033\">2. What role does machine learning play in predicting stock market movements?<\/strong><\/h3>\n<p data-start=\"1036\" data-end=\"1488\">Machine learning models use <strong data-start=\"1064\" data-end=\"1139\">historical price data, sentiment analysis, and macroeconomic indicators<\/strong> to forecast future trends. These models learn from patterns in data to improve accuracy over time. While not foolproof, AI-driven predictions help investors anticipate potential movements and make more informed decisions. However, reliance on biased or incomplete data can sometimes lead to misleading results, making <strong data-start=\"1458\" data-end=\"1487\">human oversight essential<\/strong>.<\/p>\n<h3 data-start=\"1495\" data-end=\"1575\"><strong data-start=\"1499\" data-end=\"1573\">3. Are AI-driven trading systems safe for retail investors to rely on?<\/strong><\/h3>\n<p data-start=\"1576\" data-end=\"2059\">AI-powered trading platforms\u2014like robo-advisors and algorithmic bots\u2014are increasingly accessible to retail investors. They provide <strong data-start=\"1707\" data-end=\"1741\">automated portfolio management<\/strong> and <strong data-start=\"1746\" data-end=\"1773\">personalized strategies<\/strong> based on individual risk tolerance. While these systems offer convenience and consistency, investors should remain cautious, as <strong data-start=\"1902\" data-end=\"1987\">AI cannot fully account for sudden market sentiment shifts or geopolitical shocks<\/strong>. A balanced approach combining AI insights with human judgment is best.<\/p>\n<h3 data-start=\"2066\" data-end=\"2152\"><strong data-start=\"2070\" data-end=\"2150\">4. What are the major risks of algorithmic and high-frequency trading (HFT)?<\/strong><\/h3>\n<p data-start=\"2153\" data-end=\"2607\">Algorithmic and high-frequency trading increase market speed and liquidity, but they can also <strong data-start=\"2247\" data-end=\"2269\">amplify volatility<\/strong>. When multiple algorithms respond simultaneously to market signals, it can create <strong data-start=\"2352\" data-end=\"2370\">feedback loops<\/strong>, as seen during the <strong data-start=\"2391\" data-end=\"2411\">2010 Flash Crash<\/strong>, where nearly $1 trillion was temporarily wiped out. The primary risk lies in <strong data-start=\"2490\" data-end=\"2555\">data errors, misaligned algorithms, or untested trading logic<\/strong>, which can cascade into massive market disruptions.<\/p>\n<h3 data-start=\"2614\" data-end=\"2696\"><strong data-start=\"2618\" data-end=\"2694\">5. How can investors manage the risks posed by AI-based trading systems?<\/strong><\/h3>\n<p data-start=\"2697\" data-end=\"3126\">Investors can mitigate AI-related risks by <strong data-start=\"2740\" data-end=\"2767\">diversifying portfolios<\/strong>, using <strong data-start=\"2775\" data-end=\"2800\">verified data sources<\/strong>, and avoiding overdependence on automated systems. Regulators and exchanges are also introducing frameworks for <strong data-start=\"2913\" data-end=\"2956\">real-time monitoring and stress testing<\/strong> of AI-driven systems to prevent systemic errors. For retail investors, understanding the technology behind their platforms and setting manual safeguards remains crucial.<\/p>\n<h3 data-start=\"3133\" data-end=\"3214\"><strong data-start=\"3137\" data-end=\"3212\">6. Will AI completely replace human traders and analysts in the future?<\/strong><\/h3>\n<p data-start=\"3215\" data-end=\"3664\">While AI is reshaping trading dynamics, it is unlikely to <strong data-start=\"3273\" data-end=\"3314\">completely replace human intelligence<\/strong>. Machines excel at analyzing patterns and executing trades quickly, but they lack emotional intelligence and contextual understanding\u2014key factors during volatile or uncertain market conditions. The future lies in <strong data-start=\"3528\" data-end=\"3554\">human-AI collaboration<\/strong>, where traders use AI for data insights while relying on human strategy and judgment to make final decisions.<\/p>\n<h3 data-start=\"3671\" data-end=\"3755\"><strong data-start=\"3675\" data-end=\"3753\">7. How can investors prepare for the AI-driven future of the stock market?<\/strong><\/h3>\n<p data-start=\"3756\" data-end=\"4182\">To stay ahead, investors should <strong data-start=\"3788\" data-end=\"3830\">embrace technology rather than fear it<\/strong>. Learning about AI-powered platforms, understanding algorithmic trading basics, and following regulatory developments can give investors an edge. Additionally, diversifying across <strong data-start=\"4011\" data-end=\"4030\">AI-focused ETFs<\/strong>, <strong data-start=\"4032\" data-end=\"4060\">tech-driven mutual funds<\/strong>, and <strong data-start=\"4066\" data-end=\"4101\">companies leading AI innovation<\/strong> can help capture long-term growth from this technological revolution in finance.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI in Stock Trading: Maximilian Goehmann Warns of Hidden Risks Amid Market Transformation As artificial intelligence (AI) takes over modern stock trading with unprecedented precision, experts warn that small data errors in automated systems could cause massive financial instability \u2014 echoing past crises like the 2010 Flash Crash. AI is rapidly transforming global financial markets. [&hellip;]<\/p>\n","protected":false},"author":4,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1368],"tags":[],"ppma_author":[1331],"class_list":{"0":"post-17035","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-blog"}," _eael_post_view_count":0,"authors":[{"term_id":1331,"user_id":4,"is_guest":0,"slug":"sourabh","display_name":"Sourabh Sharma","avatar_url":{"url":"https:\/\/trending.niftytrader.in\/wp-content\/uploads\/2025\/11\/Sourabh-Sharma.png","url2x":"https:\/\/trending.niftytrader.in\/wp-content\/uploads\/2025\/11\/Sourabh-Sharma.png"},"0":null,"1":"","2":"","3":"","4":"","5":"","6":"","7":""}],"_links":{"self":[{"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/posts\/17035","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/comments?post=17035"}],"version-history":[{"count":2,"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/posts\/17035\/revisions"}],"predecessor-version":[{"id":17039,"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/posts\/17035\/revisions\/17039"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/media\/17040"}],"wp:attachment":[{"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/media?parent=17035"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/categories?post=17035"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/tags?post=17035"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.niftytrader.in\/markets\/wp-json\/wp\/v2\/ppma_author?post=17035"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}