How Will The Stock Market Open Tomorrow?
It’s impossible to know 100%, however, stock futures, options markets and sites like spytomorrow.com offer prediction models which can help you understand market direction.
This is a big question. On average the stock market (S&P 500) will open up 53% of the time. This doesn’t mean it will stay up the entire session. Some days the market immediately falls, while other days it will move across the zero point multiple times during the day.
Stock market prediction is the attempt to try to determine the future value of a stock or other financial instrument traded, ETF, or a market collection such as QQQ or S & P 500. A successful prediction of a stock’s or market future price can yield significant profit.
However, efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes which are not based on newly revealed information (news) thus are inherently unpredictable.
We agree that news can change market sentiment, we certainly disagree that a near term prediction can’t be predicted with high probability.
How can I predict tomorrow’s stock market?
(thank you to investopedia for these 4 methods)
Don’t fight the tape. This commonly quoted piece sage stock market wisdom which warns investors not to bet against the market trends. The idea is that the best prediction about market movements is that they will continue in the same direction. Often they will continue to move longer than you think they should. This idea has its roots in behavioral finance. When there are so many stocks to choose from, why would investors keep their money in a stock that is falling, as opposed to one that is increasing in value.
Experienced investors, who have seen many market ups and downs, often take the view that over time the market will even out. History says, high market prices often discourage these investors from investing, while historically low prices can represent an opportunity.
A possibility is that past returns really don’t matter. In 1965, Paul Samuelson studied market returns and found that past pricing trends had no effect on future prices and reasoned that in an efficient market, there should be no such effect. His conclusion was that market prices are martingales.
A martingale is a mathematical series in which the best prediction for the next number is the current number. The concept is used in probability theory, to estimate the results of random motion. For example, suppose that you have $50 and bet it all on a coin toss. How much money will you have after the toss? You may have $100 or you may have $0 after the toss, but statistically, the best prediction is $50 – your original starting position. The prediction of your fortunes after the toss is a martingale.
Looking for Value
The Value investor buys stock cheaply with the expectation to be rewarded later. Their hope is that an inefficient market has underpriced their stock selection and the price will adjust over time. The question is: does this happen, and why would an inefficient market make this adjustment?
Research does suggest that mispricing and readjustment consistently happen in the market, although the research presents very little evidence as to why it happens.
The Average Daily Percent Move In The Stock Market
Below is an excellent chart which shows the daily percentage movement of the S&P 500 over a ten year period. Each dot represents a single day.
As you can see from the above chart, the average daily move in the stock market is between -1% and +1%. The S&P 500 represents the stock market.
So, if you are a long-term investor in the focus of capital accumulation, you should consider buying more than your normal investing budget when the S&P 500 is down greater than 1%.
If you’re in capital preservation mode, you may consider selling some of your S&P 500 index position when the S&P 500 is up greater than 1%.
Many say, and rightly so, trying to beat the S&P 500 over the long run through market timing is almost impossible. Of course, we see different results here with our spy tomorrow model. However, just guessing or timing because the market is up or down generally doesn’t work.
There Is More Than One Way To Predict The Stock Market
It’s no surprise to use, the results for what the stock market might do tomorrow can be predicted with a fairly high probability using some very basic observations (source).
Many studies have examined what is called “Daily Price Persistency”. These show that the action of the market today has a definite influence on what the market might do the next day. The following are the probabilities of the movement of tomorrow’s market based on the action of today’s market.
If today’s market is up…there is a 73% probability of tomorrow’s market being up again tomorrow and a 27% probability that tomorrow’s market will actually close down.
If today’s market is down…there is a 62% probability that tomorrow’s market will also be down too, and only a 38% probability that the market will close higher.
It’s interesting to see that the odds of the market closing higher after a negative day are better than closing down after a positive day.
Historically, the market has advanced on 58% of all market days, demonstrating its overall historic upward bias. This number depends on which market you are following. Most seem to land in the 50%-60% range.
There is another factor that can improve predictions just a little bit more. When you examine the last 15 minutes of trading action and figure the direction of that last 15 minutes we gain additional insight into tomorrow. If the last 15 minutes of market action is positive and the market does close higher, then the odds of tomorrow being an up day improve to 77%. On the other side, if the last 15 minutes of the market is negative on an overall down market day, the odds of the market going down the next trading day increase to 73%.
These statistics show that although the markets do have a high degree of efficiency, there still is a trend to follow when it comes to the buying and selling of stocks. These trends occur in periods as short as one day and for periods lasting several years.
Stock market prediction is a major interest in the field of finance and establishing businesses. The stock market prices fluctuate on a daily basis because of numerous factors that influence it.
One of the traditional methods of predicting stock prices is using historical data. As mentioned above some studies suggest that doesn’t work while others say stick with the trend.
However, with time it was observed that other factors such as peoples’ sentiments and other news events occurring in and around the country affect the stock market, for e.g. national elections, natural calamity etc. Investors in the stock market seek to maximize their profits for which they require tools to analyze the prices and trends of various stocks.
Machine learning algorithms are used to devise new techniques to build prediction models which can forecast the prices of stock and predict the market trend with good accuracy.
There are many prediction models that have been proposed to incorporate all the major factors affecting the price of stocks. One focus is portraying distinct machine learning algorithms such as support vector machine, deep learning, random forest, boosted decision trees, ensemble methods, and a few hybrid methods which have been used to build prediction model and predict the stock prices for different stock exchanges.
Other insight to consider
As we know, an accurate stock market prediction is of great interest to investors, yet, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data.
The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms based on information found in social media and financial news, as this data can change investors’ behavior.
Some algorithms use social media and financial news data to discover the impact of this data on stock market prediction accuracy for ten subsequent days. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets.
It is interesting that some find in their experiments some markets are more influenced by social media and financial news than others. They try to compare the results of different algorithms to find a consistent classifier.
They seem to achieve maximum prediction accuracy when deep learning is used and the best performing classifiers are ensembled.
Some experimental results show that the highest prediction accuracies of 80.53% and 75.16% are achieved using social media and financial news, respectively. They also show that New York and Red Hat stock markets are hard to predict, New York and IBM stocks are more influenced by social media, while London and Microsoft stocks by financial news. Random forest classifier is found to be consistent and the highest accuracy of 83.22% is achieved by its ensemble.
I can tell you, we don’t use machine learning for our prediction model and we match up fairly well to the top technology and prediction models.