Questions tagged [prediction]
The prediction tag has no usage guidance.
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How can I go about applying machine learning algorithms to stock markets?
I am not very sure, if this question fits in here.
I have recently begun, reading and learning about machine learning. Can someone throw some light onto how to go about it or rather can anyone share ...
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Any research on how natural language processing can be used to forecast stocks?
Is there any published research of decent quality linking news or unstructured information to asset returns? I know that Thomson Reuters offers its Machine Readable news (MRN), so somebody must use it....
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How to incorporate technical indicators into neural networks?
I plan to develop a neural network to trade commodities futures, but while messing around with some code, a question came up. If I understand correctly, people use various technical indicators with ...
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Using linear regression on (lagged) returns of one stock to predict returns of another
Suppose I want to build a linear regression to see if returns of one stock can predict returns of another. For example, let's say I want to see if the VIX return on day X is predictive of the S&P ...
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Multilayer Perceptron (Neural Network) for Time Series Prediction
I have it in mind to build a Multilayer Perceptron for predicting financial time series. I understand the algorithm concepts (linear combiner, activation function, etc). But while trying to build the ...
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GARCH model and prediction
I have a question about the prediction of volatility and returns of a time series. Basically it is a question about predict in the ...
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Tools/R code for predicting Dragon-Kings
The theory of the so called Dragon-Kings, esp. by Didier Sornette (ETH Zürich), basically states that financial crises and crashes are predictable (contrary to the theory of black swans).
The ...
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Can the futures market's open interest predict commodity, treasury, and equity returns?
I came across this article and became curious. Can the futures market's open interest really predict market action?
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Determine trends of data (direction detection or turning point detection)
I'm working on a model to determine trends (direction detection or turning point detection). Suppose that we have a stock trend which is illustrated below. Blue line is real trend of stock close ...
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ARMA+GARCH prediction with package rugarch (R)
I am analyzing FTSE 100 series, from 2007-01-01 to 2010-12-31 (university exam homework).
I have to use the data 'til 2010-11-30 as sample, and the remaining (23) observations as in-sample forecast (...
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What machine learning method is more suitable for prediction of financial time series?
I have time series data for various assets and which I transform to create various features. I have framed the problem as a classification task where I attempt to predict either a positive or negative ...
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Are returns predictable, Campbell and Shiller (1988)
Following from the thread,
Drivers of equity returns: dividend yield, change in P/E and dividend (or earnings) growth
1) Why are returns predictable from this, is there a reason?
2) Can we expect ...
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Time series strategy versus cross section strategy?
Suppose we have a universe of $n$ stocks, and for each time period $t$ we have $n$ predictions for their future returns. Now we can calculate the information coefficient for our predictions in two ...
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The T+H Problem in Factor model forecasts
Suppose we train on M individuals consisting of T observations (i.e. TxM design matrix). The dependent variable is one-year return for each security (H = horizon of one year). In a factor model ...
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Do futures have predictive value?
Futures closely mirror their underlying, as can be seen in the charts below. Eventually, at expiration, they reach the value of the underlying. However, they seem to show no extra information about ...
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Using Black-Scholes equations to "buy" stocks
From what I understand, Black-Scholes equation in finance is used to price options which are a contract between a potential buyer and a seller. Can I use this mathematical framework to "buy" a stock? ...
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Predict Market Direction, What is forecastable/unforecastable?
Let's decompose the return process $R_t$ as follows :
$$R_{t} = sign(R_{t}) * |R_{t}| $$
What's part of the equation is forecastable?
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Quant teams predicting the World Cup
It is a good tradition of the quant teams of the major banks to predict the World Cup. As an example see this new paper from Goldman Sachs:
The World Cup and Economics 2014 (Brazil will win by the ...
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How many explanatory variables is too many?
When researching any sort of predictive model, whether using ordinary linear regression or more sophisticated methods such as neural networks or classification and regression trees, there seems to ...
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how are financial data with sparse and asynchronous features imputed in predictive modeling?
I watched a presentation from a large quantitative finance firm that spends a lot of effort around predictive modeling. One of the points the presenter emphasized was that they deal with a lot of ...
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Does predictability in a VAR process imply mean reversion or momentum?
There seems to be some disagreement in the literature about this. Define predicability of a stationary series to be $\sigma^2_{t-1} / \sigma^2_t$
Finding mean reverting portfolios using canonical ...
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What is time-varying risk premium? Forecasting stock returns
I am trying to understand the concept 'Time-varying aggregate risk premium'.
Here is an extract from a Forecasting book, written by Rapach and Zhou,
"However, rational asset pricing theory posits ...
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Predict probability of returns: How does changing volatility affect the return pdf?
I am trying to predict the future probability of stock returns based on the return distribution. Therefore I calculate the returns as $\frac{P(t)}{P(t-1)}$ for the whole daily data and fit a ...
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How quants use ML models for stock market prediction
I am learning machine learning to use it for stock market price forecasting. While doing that I got this question. If we take any country with stock exchange they have more than one investment assests ...
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Interpret predictions weekly and monthly stock price returns [closed]
I have built a model in R that predicts weekly and monthly returns of stock prices using regression trees, roughly based on https://www.r-bloggers.com/using-cart-for-stock-market-forecasting/. In my ...
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Predict Futures Prices based on weather + agricultural data
I’m working in the area of Data Mining and have come up with the following idea for my Masters project.The text may not be the best structured but it’s a working draft to give you a quick idea.
...
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Implementing Hanson`s LMSR with Limit Orderbooks
I am trying to integrate Hanson's LMSR (see (see logarithmic market scoring rule)into an order-book with traditional bid/ask-limit orders (in KDB+/Q).
The following functions define the basic LMSR ...
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What is the stambaugh bias? Why is it important for predictability regressions?
What is the Stambaugh bias? Why is it important for predictability regressions?
Can anyone explain it in simple terms?
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Any research on label/target variable design for ML training?
is there any discussion or paper about how to define/design the labels for the ML training? Intuitively I can think of:
Net return of the next future day
Net return using the max candle-high value of ...
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Principal Portfolios Prediction Matrix estimation (Bryan Kelly)
I have recently discovered Bryan Kelly's paper on Principal Portfolios (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3623983) and had some doubts about the prediction matrix $\Pi$. He defines $\...
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Continuous prediction vs Event-based predictions
When making a high-frequency or mid-frequency prediction on an assets return, what are the advantages and disadvantages of making a continuous prediction vs a prediction that only fires on a ...
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Algorithms for predicting a couple points in the future
I'm familiar with supervised learning algorithms like regression and neural networks which look at a bunch of input points and learn a function which outputs a value (the value varying depending on ...
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Technical Indicators reference
I have been looking for a good reference where I can find how technical indicators of stock market analysis are calculated. I have a dataset (time series) which I want to extract these indicators to ...
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Which prediction market model is efficient and simple to use?
For a college project I'm tasked with implementing prediction market. Which model of it I'd better choose?
I want something useful and simple enough for other people to quickly understand and use. (...
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Predicting portfolio returns
I suppose there are roughly two approaches to predict portfolio returns.
Either predict the returns of all underlying stocks and aggregate all individual stock predictions, or predict the portfolio ...
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Paper on returns from perfect market timing?
I'm looking for a (free) paper I read which showed that even a "perfect" market timing strategy wasn't very good compared to buy-and-hold. There were some restrictions to the timing, ...
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time series data modeling for deep learning
what is the best format to feed the input data, which are time series with varying density over time, to a deep learning network, while at any iteration we want to feed a batch of data including a ...
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What is the better representative of a P-B ratio for a sector?
What is a better representative of a P-B ratio for a sector, for using it as a factor to predict future returns on that sector? The market weighted average of P-B for all names in that index, or ...
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Implementing A 50/50 Prediction Model Strategy
Reworded the question for clarity (see edits for original post):
How can one knowingly foresee where a 50/50 prediction model will be profitable? For previous posts: I understand that if I have a 50/...
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Good criteria to sort state-space $\beta_{t}$ according to Kalman filter output
Let's assume the usual state-space linear model without constant term for simplicity:
$y_{t}=\beta_{t} X_{t}+\epsilon_{t}$
If we apply Gaussian Kalman filter to estimate $\beta_{t}$ we get $P_{t}$, ...
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Oscillatory time-series forecasting
I was wondering if this mean(160)-reverting/oscillatory time series "SUM" can be considered chaotic & forecastable to some extend short-term?
http://sg.myfreepost.com/sgTOTO_analysispower.php?...
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One-day-ahead prediction of S&P500 with Temporal Convolutional Networks
I'm trying to predict the one-day ahead movement of the S&P 500 with Temporal Convolutional Networks 1 to capture some "memory".
I use daily close data with the loss function $\mathrm{...
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What is currently predictable in the stock and bond markets and what is not
Disclaimer: I have some knowledge of statistics, machine learning and probability theory, but next to zero knowledge of finance (I had to look up Wikipedia to refresh my knowledge of the difference ...
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Using candlesticks for Stock price direction prediction
I am working on a college project wherein I want my machine learning model to predict the one-day-ahead direction of a given stock (i.e. whether the closing price of the stock would rise or fall as ...
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Fractal market hypothesis testing
I would like to do an analysis on the AEX stock exchange index for the last 20 years, but I ran into some issues. It would be really appreciated if you can answer my questions:
In order to apply ...
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Is there a general approach to predicting future (vanilla) option prices in practice?
I realize that this question may be verging on asking for the proprietary/"secret", so if suggestion of a general approach that doesn't divulge details isn't really possible, I understand.
...
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Constructing a mid using signals from another asset
When delta-neutral market making it is important to construct a mid price. Often the mid price of the asset you are trading is influenced by another (correlated) asset. What methodologies would you ...
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Prediciting outperformance - choice of statistical design?
I want to predict relative outperformance between a stock and an associated benchmark index using statistical time-series models (e.g. ARIMA) and some exogenous variables (day of the week, corporate ...
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Window length for predictive regressions
I am building a trading strategy that predicts the current period returns using historical returns (think e.g. using an estimated OLS model to predict next weeks return based on this weeks return). ...
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predict next day's close price using hmm
I am reading this paper(Stock market forecasting using hidden Markov model: a new approach) and get confused about how they predict the next day's close price. Below is what the authors say about how ...
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