#Linkedin stock projections professional#
#Linkedin stock projections free#

The current model is narrow is scope and does not factor in implications of macroeconomic indicators such as Real GDP, inflation, or interest rates which can have drastic effects on some stocks.As much as you challenge it, some data sets are better to work with than others. As stock prices behave differently, stock prediction accuracy will also vary.Better returns will come with more diverse and information-rich features. Stock prices during this time frame were greatly influenced by macroeconomic factors which my model does not factor in. My model does not produce the best results, but I do believe it’s a strong start. Similarily a buy and hold strategy in the S&P500 during the same time frame would have resulted in -6.9% in the same time frame (due to recession) and Amazon’s stock would have performed 152%. My model was able to produce an average ROI for 1.3% per month from 2008–2010, equal to 31.2%. The model will tell me to sell the stock if next day prices are going to decrease, buy if next day prices will appreciate. Since the SVC had the highest accuracy levels, I’ve used it to model this strategy. For the time being, I have devised a trading strategy based on the current model to analyze how well I would do in the stock. The addition of more features can help me filter the noise to reach these levels.

#Linkedin stock projections professional#

Professional quant traders on Wall Street and Bay Street achieve up to 55% accuracy in predicting next day stock prices, and up to 80% accuracy in predicting stock prices 30-days out. The spread between the null model and the other models help describe how efficient each model is.

The Null accuracy is inversely related to the forecast period. It’s to no surprise that next day predictions are not much better than the odds of correctly predicting a coin toss (observed by tracking the null accuracy). The longer the forecasted horizon, the more accurate our predictions become. I’ve used 80% of the data for training and 20% for testing.

#Linkedin stock projections free#

The data contains daily stock information from - current (because I’m on the free plan, the data is always, at least, one week behind). I have used one of my favorite stocks, Amazon (NYSE: AMZN) to model the algorithm. The training data used in my project was collected from Quandl Database. This has always been a passion topic for me, so here goes nothing… After all I am only following the plethora of algo and speculative traders that continue to exploit the market in the short term. My hypothesis is simple: By mining for patterns in data using supervised machine learning techniques, I can construct a model and trading strategy that beats the market. Lots of research has already gone into figuring out how stock prices move like here and here. The efficient market hypothesis tells us that all relevant information is already factored into a stock’s price, meaning that neither fundamental or technical analysis can be used to achieve superior gains in the short and long-term. I’m a believer that over the short term (under 1 year) stock prices move in wave patterns - understanding these, can help us understand stock price movements. I’ve tried many different strategies and put a lot of thought trying to come up with an adequate strategy to consistently make money by investing in the stock market. Since my late high school years, I’ve reaped a deep interest in financial markets. Part 3 - The Finance: Inferences in Stock Behavior.Part 2 - The Math: Applying Supervised Machine Learning (code attachment here).Part 1 - Overview: Using Machine Learning to make data-driven decisions.Data science and model construct performed using Scikit-learn, numpy, and pandas packages. This post will take you inside the works of a 4 month project on developing a machine learning algorithm for stock predictions under the supervision of Schulich Professor Zhepong (Lionel) Li.