Questions tagged [prediction]
The prediction tag has no usage guidance.
94
questions
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50 views
Normalization of cryptocurrency / stock price data
I am making predictions on cryptocurrency / stock price data. I am using the candlestick data (High, Low, Open, Close) to estimate the close price of the next day. I am looking for the right way to ...
0
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1answer
47 views
Non-fixed stationary “conversion”
Dear users of StackExchange,
I was wondering why the log returns of a fixed period of time is such a common use in "transforming" a time series into a more stationary one?
I thought that ...
2
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1answer
67 views
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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0answers
50 views
How to use machine learning to generate optimal allocations for an instrument?
What is the idea behind using Machine Learning in finance? Let's assume that we have just one instrument given by its prices. At a given moment of time, we can "compress" the available ...
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45 views
Why are prediction markets based on logarithms when a linear solution can suffice?
For example, take a binary outcome; A coin toss, heads or tails.
If heads, then those that picked heads receive \$1 and tails receive \$0.
To quote the prices for each bet Hanson's LMSR uses ...
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22 views
Is it possible to compare Forex data to similar random time series to measure how predictable it is?
In relation to my previous question (Who influences Forex prices and by how much?) I have an raw idea how to determine how much is Forex influenced externally and how much is its behavior given by its ...
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57 views
Please help me understand this dataset regarding stock prices
I am supposed to predict column E but I cannot figure out what any of these columns mean. The information provided with the dataset is as follows:
column A: past 28 week slope value
column B: past 48 ...
0
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1answer
97 views
Feature engineering for mid-price prediction - quickly changing features
I'm training a fully-connected feed-forward neural network on HFT (limit order book) data to predict the midprice at timepoint $t+\Delta t$ (assuming that $t$ is the current moment, and $\Delta t$ is ...
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3answers
178 views
Consistent offset/lag in time-series prediction using Neural Network (all code provided)
I'm using a neural network (keras package) to predict Bitcoin prices 48 hours in advance. The issue is that for some reason, my predictions are "correct" but they are lagging behind the true ...
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1answer
114 views
Reintegrating Fractionally Differentiated Time Series Prediction
I am working on a supervised learning approach to Time Series Regression, and am currently investigating fractionall differentiation (optimizing the stationarity/information tradeoff) discussed ...
2
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0answers
64 views
Garch models - are they useful for hedging? If so how?
I understand that Garch models are useful to predict volatility. But are they useful for hedging in practice? If I want to hedge volatility, why shouldn't I just use a Variance Swap?
In other words, ...
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1answer
119 views
Predict Log Stock Return Direction and Trading Strategy
The $k$ period log return is defined as $$r_{t}(k)=log(S_{t}/S_{t-k}),$$ Where $S_{t}$ is the stock closing price at time $t$. For argument sake, assume that by time I mean a stock trading day and ...
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1answer
71 views
Is option surface same as future price probability surface?
Let's consider the Option Chain for the Stock. There are two 3D surfaces representing the probability of the future stock price and the option prices. I wonder if they are representing the same thing?
...
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21 views
Tradeoffs of using Loess regression to fit random walks
I am curious if anyone has had much experience attempting to predict random walks using Loess regression or a variant of local statistical methods.
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3answers
118 views
Does asset volume, rather than asset returns, predict performance?
Asset returns are the most common data type used in finance. They are derived from closing price data. Ordinary level 1 data for stocks not only consists of closing prices, but also gross volume ...
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1answer
37 views
Mutual fund rating predictions
I am working on a dataset with aim to predict the MF ratings. There are cols like, 10 yr, 7 yr, 5 yr etc returns. I also have commencement date of MFs, the question is there are MFs with commencements ...
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1answer
65 views
How to use multi-periods and mult-factors to predict stock price by linear regression?
Give data in $t_n$ denoted by $[x_1^n, x_2^n, ... x_d^n]$ and label $y_n$ to be predicted. We can just train a $d$-dimensional linear regression $y_n=\sum b_ix_i^n$ to make a prediction. However, I ...
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1answer
64 views
If a security has many options expiring on a day, can you predict its price direction?
r/wallstreetbets post alleges that because ~$1.9 Trillion USD of SPY call options expire on 3/20, the price of SPY will skyrocket on 3/20.
Is this correct? What can be deduced? I'm not expecting to ...
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1answer
73 views
Predicting time series based on another
This is more of a generic question, but I'm sure it has a best answer/methodology which is what I'm trying to reach. I'm trying to figure out a solid line of thought when looking at a time series X ...
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1answer
52 views
Relationship between Data Size and Arima Prediction Interval Width?
When we use Arima model to acquire Interval Predictions, will the width of prediction intervals decrease if we use more data (longer history) to fit the model?
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2answers
46 views
Updated Time Series Prediction Model When acquiring new data Points - Basic Question
Suppose I have a Time Series Model (assume ARIMA) and use it to make one-step ahead prediction.
If I acquire a new data point, (for example I was originally using the first 100 days to fit an Arima ...
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0answers
63 views
Replication of the paper: “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction”
I recently replicated the paper "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction" and found out that my estimation of the equity premium differs from the data provided ...
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1answer
219 views
Question is about the data in the paper: “A Comprehensive Look at The Empirical Performance of Equity Premium Prediction”
I would like to ask a question if you download the data from the Amit Goyal website:
http://www.hec.unil.ch/agoyal/
You will see that there are two columns "CRSP_SPvw" "CRSP_SPvwx", they are ...
1
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1answer
168 views
Proper Data Partitioning For Building a Forecasting Model
Goal: A team and I are looking to build a model that performs a predictive action for the state of the market on day T + n, using the data at hand on day ...
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0answers
108 views
How to weigh computational cost of updating an online predictive model for latency-constrained trading (e.g., market making, HFT)?
Say one has a predictive online model for market making or HFT (or just for anything strictly latency-constrained). In my specific example, I start with a Gaussian distribution over the "true value" ...
3
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1answer
152 views
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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0answers
28 views
Which technique determines if var x1 leads var y? Assuming var x1 may need to be transformed
Suppose I want to predict future changes in variable y (stock price over time). I notice that variable x1, inverted and delayed three months, tends to lead y. Which technique can I use to find other ...
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0answers
357 views
Beginner FFT (Fourier) transforms on closing prices for Apple
I don't know math very well, but I have been programming for many years.
I would like to use FFT as a parameter to a ML model. The FFT is diving down sharply. I tried many stocks and its the same.
...
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0answers
46 views
Labeling Returns in 5 categories based on BL view approach
I have to label a time series of returns into 5 categories based on the Black Litterman view approach.
The categories should look as follows:
very bullish: + 2 std. dev.
bullish: + 1 std. dev.
...
2
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0answers
101 views
Mean directional accuracy and zero
I'm trying to use mean directional accuracy to evaluate my directional predictions in back-test, but it can't deal with realised directions which are 0, due to the comparison of the signs of ...
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0answers
64 views
Predicting stock returns using principal components of macroeconomic variables
I'm trying to detect return predictability by regressing stock returns on the first couple of principal components of a set of macroeconomic variables. I'm doing this for different stock styles such ...
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4answers
304 views
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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0answers
89 views
Can I make the simple moving average less lagging by this method?
I have (T+3) predicted prices for a stock. Let us assume that the predicted prices are going to be very close to the actual prices. can I alter the formula of simple moving average for 20 days like ...
2
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0answers
69 views
Why can the t-bill rate forecast stock returns?
The tbill rate is used as a predictor of the equity premium in a number of papers.
Whilst there is not a general consensus about whether it is a significant predictor, it is still widely used.
I ...
3
votes
2answers
255 views
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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1answer
187 views
Should there be a relation between stocks when used as input data for integrating Technical Analysis with Machine Learning?
I'm integrating Technical Analysis with Deep Learning for the first phase of my research. I wanted to know how should I pick (or group) stocks as input data and whether there should be relation ...
6
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2answers
459 views
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 ...
5
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0answers
230 views
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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0answers
57 views
Trading Signals with Different Lags
I have a momentum signal that gives best predictive power at 3 months and a valuation signal that gives best predictive power at one year. If I combine for a 3 month horizon by "interpolating" the ...
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1answer
291 views
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 ...
1
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2answers
369 views
Working with time series with different resolutions
I’m looking at making predictions of close prices (stocks/commodities), and have access to various data sources to help predict.
However, most of these sources are in a different time frame, ...
5
votes
1answer
242 views
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 ...
2
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2answers
215 views
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 ...
4
votes
1answer
824 views
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?
4
votes
2answers
2k views
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 ...
0
votes
1answer
182 views
Momentum Analysis on Indices
I'm interested in analysis of day-on-day momentum of certain large indices. In particular, I'm interested in the predictive power of the sign of the price change of the first hour of trading with ...
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1answer
448 views
Predict the behavior of a time series (P&L trading desk)
I work at the trading desk P&L department at a large bank.
The trading desk has positions in almost all sorts of derivatives (options, futures) over a long list of stocks, currencies, commodities.....
5
votes
1answer
452 views
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 ...
11
votes
3answers
5k views
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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0answers
492 views
R squared statistic in predictions of returns
My question is related to an article which use predictive linear regression for the stock returns. There is told that R squared statistic of 1.6% is high. How can we measure which R squared is high? I ...
