Accurate Market Predictions

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Accurate Market Predictions

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You guys are doing a great job

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Who are the most (least) accurate stock gurus?

By Larry Swedroe

February 13, 2020 / 8:47 AM / MoneyWatch

(MoneyWatch) The financial media tends to focus most of its attention on stock market forecasts by purported investment gurus. They do so because they know that’s what gets the public’s attention. Investors must believe they have value or they wouldn’t tune in. Nor would they subscribe to investment newsletters, nor publications like Barron’s that claim to provide you with “news before the markets know.”

Unfortunately for investors, there’s a whole body of evidence demonstrating that market forecasts have no value (though they provide me with plenty of fodder for my blog) — the accuracy of forecasts is no better than one would randomly expect. For investors who haven’t learned that forecasts should only be considered as entertainment, or what Jane Bryant Quinn called investment porn, they actually have negative value because forecasts can cause them to stray from well-developed plans.

The latest piece of evidence illustrating the futility of forecasts comes from CXO Advisory Group. The investor research firm set out to determine if stock market experts, whether self-proclaimed or endorsed by others, provide useful guidance on how to time the stock market. To find the answer, from 2005 through 2020 they collected and investigated roughly 6,600 forecasts for the U.S. stock market offered publicly by 68 experts, bulls and bears employing technical, fundamental and sentiment indicators. Their collection included forecasts, all of which were publicly available on the Internet and which went back as far as the end of 1998. They selected experts based on Web searches for public archives with enough forecasts spanning enough market conditions to gauge their broader accuracy.

CXO’s methodology was to compare forecasts for the U.S. stock market to the S&P 500 index returns over the future intervals most relevant to the forecast horizon. They excluded forecasts that were too vague and forecasts that included conditions requiring consideration of data other than stock market returns. They matched the frequency of a guru’s commentaries (such as weekly or monthly) to the forecast horizon, unless the forecast specified some other timing. And importantly, they took into account the long-run empirical behavior of the S&P 500 index. For example, if a guru said investors should be bullish on U.S. stocks over the year, and the S&P 500 index was up by just a few percent, they judged the call incorrect (because the long-term average annual return has been much higher). Finally, they graded complex forecasts with elements proving both correct and incorrect as both right and wrong (not half right and half wrong).

The following is a summary of CXO’s findings:

  • Across all forecasts, accuracy was worse than the proverbial flip of a coin — just under 47 percent.
  • The average guru also had a forecasting accuracy of about 47 percent.
  • The distribution of forecasting accuracy by the gurus looks very much like the proverbial bell curve — what you would expect from random outcomes. That makes it very difficult to tell if there is any skill present.
  • The highest accuracy score was 68 percent and the lowest was 22 percent.

There were many well-known forecasters among the “contestants.” I’ve highlighted 10 of the more famous, most of whom I’m sure you’ll recognize, along with their forecasting score.

  • James Dines, founder of The Dines Letter. According to his Website, “he is truly a living legend. one of the most-accurate and highly regarded Security Investment Analysts today.” His forecasting accuracy score was 50 percent. Not quite the stuff of which legends are made.
  • Ben Zacks, a co-founder of well-known Zacks Investment Research and senior strategist and portfolio manager at Zacks Wealth Management Group. His score was 50 percent.
  • Bob Brinker, host of the widely syndicated MoneyTalk radio program and editor of the Marketimer newsletter. His score was 53 percent.
  • Jeremy Grantham, Chairman of GMO LLC, a global investment management firm. His score was 48 percent.
  • Dr. Mark Faber, publisher of the Gloom, Boom and Doom Report. His score was 47 percent.
  • Jim Cramer, CNBC superstar. His score was 47 percent.
  • John Mauldin, well-known author. According to his Website, “His individual investor-readers desperately need to know what his institutional money-manager clients and friends know about the specific investments available to help them succeed in challenging markets.” His score was just 40 percent.
  • Gary Shilling, Forbes columnist and founder of A. Gary Shilling & Co. His score was 38 percent.
  • Abby Joseph Cohen, partner and chief U.S. investment strategist at Goldman Sachs. Her score was 35 percent.
  • Robert Prechter, president of Elliott Wave International, publisher of the Elliott Wave Theorist and author of multiple books. He brought up the rear with a score of 22 percent.

Of course, there were a few with fairly good records. But only five of the 68 gurus had scores above 60 percent (among them was David Dreman with a score of 64 percent), yet 12 had scores below 40 percent. It’s also important to keep in mind that strategies based on forecasts have no costs, but implementing them does.

The bottom line is that the research shows that whether it comes to predicting economic growth, interest rates, currencies or the stock market, the only value of gurus is to make weathermen look good.

Keep this in mind the next time you find yourself paying attention to the latest guru’s forecast. You’re best-served by ignoring it. As I point out in my latest book, that’s exactly what Warren Buffet does himself and what he advises you to do — ignore all forecasts. They tell you nothing about the direction of the market, but a whole lot about the person doing the predicting.

Image courtesy of Flickr user Images_of_Money

First published on February 13, 2020 / 8:47 AM

© 2020 CBS Interactive Inc.. All Rights Reserved.

Larry Swedroe is director of research for The BAM Alliance. He has authored or co-authored 13 books, including his most recent, Think, Act, and Invest Like Warren Buffett. His opinions and comments expressed on this site are his own and may not accurately reflect those of the firm.

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Aug 27, 2020 (The Expresswire) — The deep learning predictive AI algorithm developed by I Know First has shown an accuracy of up to 95% in its predictions for Facebook (FB).

The deep learning predictive AI algorithm developed by I Know First, a Fintech company that provides state of the art self-learning AI-based algorithmic stock market forecast solutions to uncover the best investment opportunities, has shown an accuracy of up to 95% in its predictions for Facebook (FB). That is according to aFacebook stock forecastevaluation report published by the company on August 25, 2020. The algorithm has demonstrated a higher accuracy rate for longer-term forecasts, as is often the case for predictive AI.

“We provide AI-based forecasts for different time horizons. Machine learning algorithms do better on longer time horizons: the longer the time period, the greater the statistical significance and the greater the accuracy.” said I Know First CEO Yaron Golgher. “The algorithm is able to identify the trend, and to filter out the background noise. The longest forecast is for a year, but we also have a forecast for three month, and even for three days.“

Previously, the company also released itsApple stock forecastandSandP 500 and Nasdaqevaluation reports, which also revealed a high accuracy rate.

“The accuracy varies for different assets. Some asset are more predictable, some less so. The customer is receiving forecasts on a daily basis along with the latest investment opportunities, and knows this predictability indicator in advance. For example, Facebook stock is more predictable than Snapchat. In general, a predictions success rate above 51% can allow one to make gains even on fully-automated short-termalgorithmic tradingas long as the portfolio is diversified enough,” CEO Golgher said.

Facebook Stock Forecast Overview

The Facebook stock forecast evaluation report covers the predictions for FB stock delivered by the I Know First AI algorithm in the period from January 1st to July 9th, 2020. The review period saw FB go on a gradual climb from $135 to around $200, bouncing back every now and then on the back of US-China tensions and various controversies regarding Facebook’s privacy and other policies.

Assessing the accuracy rates for forecasts ranging from 3 days to 3 months, the evaluation report covers most of the time horizons that the I Know First AI delivers predictions for. The short-term accuracy stood at around 60%, reflecting the increased volatility that FB was shaken by, but the 1-month and 3-months predictions demonstrated a significant increase in accuracy, guaranteeing the company’s clients the highest degree of certainty.

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The exact hit ratio for each of the time horizons can be found in the sheet below.

Facebook Inc. is a brainchild of Mark Zuckerberg and his Harvard roommates. A titan among American high-tech companies, it operates a variety of social media platforms; Facebook, its eponymous flagship social network service, boasts world’s highest number of users, which currently stands at around 2.23 bln. The company’s stock is immensely popular with international investors and is seen as one of the top blue-chip assets to pick.

The I Know First AI-Based Stock Market Forecast

The deep learning stock market forecast AI algorithm developed by I Know First has been trained on a dataset covering 15 years of trading. Deep learning is a term reserved for more complex artificial neural networks, ones in which the input data goes through a whole lot of transformations before the AI algorithm gives the appropriate output. The algorithm is given new trading data to process every day, making sure its output reflects the up-to-date state of the market.

The stock market forecast AI algorithm takes a holistic approach to the market, viewing it as a chaotic dynamic system, in other words, one highly sensitive to the initial conditions, where a seemingly small event can have tremendous repercussions. In doing so, it draws on the chaos theory to make sure that its models reflect this statistical disposition.

The algorithm also incorporates elements of genetic coding, keeping track of its own successes and failures and re-configuring its models if necessary. This ensures that the accuracy of its predictions goes up with every new iteration and helps it adapt to new market conditions, including periods of volatility and uncertainty like the one that the world is currently going through.

This design effectively eliminates any human bias in the system: every stock market forecast delivered is purely empirical in nature, as they are based on objective qualitative data and advanced statistical computations. The AI algorithm does not read the news, instead keeping track of the trends and feedback loops in the data, and is not prone to emotions that can sometimes mean downfall even for an experienced trader.

The forecasts are delivered as an easy to interpret heatmap, which includes two numerical indicators, signal and predictability. The former indicates if a given asset is expected to go up or down, while the latter demonstrates how good of a job the algorithm has been doing in its past predictions for this specific asset. Picking out the assets with the best indicators allows the investors to profit with the highest consistency, while keeping the risks at bay.

The AI algorithm delivers forecasts for over 10,500 assets, including stocks, ETFs, currency pairs and interest rates. Its predictions cover a wide range of time horizons, varying from 3 to 365 days, which allows it to be a worthy assistant in making decisions both on short-term trading and on long-term investment.

The AI Algorithm was developed by Dr. Lipa Roitman, a scientist with over 20 years of research and experience in artificial intelligence (AI) and machine learning (ML) fields, who leads our Research and Development team to further develop and enhance the algorithm. Dr. Lipa Roitman is an RandD Chemist with a long record in computer modeling of processes, product development and process development. The concept of the current algorithm has crystallized following years of prior research into the nature of chaotic systems. His unique RandD team consists of PhD’s and AI and Machine Learning experts, including IDF intelligence veteransand consults withProf. Yakov Yakubov, a mathematician from Tel Aviv University.

The MarketWatch News Department was not involved in the creation of the content.

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