Brokerage account

Brokerage account

Cash accounts are used by customers who pay in full for the cost of the securities purchased. Margin accounts are used by customers who are authorized to borrow part of an investment's total purchase cost from their brokerage firm. This loan from the brokerage firm to the customer is secured by the value of the securities in the customer's account. Customers generally use margin to expand their purchasing power. However, customers who use margin also run the risk that if the value of the securities that secure the margin loan declines beyond a certain level, additional money or securities must be deposited to the account in order to make up the value. A brokerage firm may sell part or all of any securities held in the account, without prior notice to the customer, in order to make up the value and meet the margin limit requirements. These "margin calls" may occur suddenly and investors should take care to understand the financial impact that trading on margin can have on the value of their accounts.

What kinds of securities can I buy online?

You can buy almost any type of stock, bond, or mutual fund online.

What's the difference between a market order and limit order? Is one better than the other?

With a market order the customer instructs his or her brokerage firm to buy or sell a stock at whatever the price is when the trade is executed, presumably as soon as possible. If the price of the stock is moving quickly and there is a delay in the transmission of the order, then the price at which the customer purchases or sells the stock may be very different than what the customer expected when the order was placed. With a limit order, the customer specifies the price at which he or she is willing to buy or sell. Limit orders can help protect customers from rapid price changes when markets are moving fast. However, there is the risk that the limit order will not be executed. Also note that limit orders usually cost a bit more than market orders.

How do I know my brokerage firm received my order?

High Internet traffic, market volume, and other systems issues may affect your ability to access your account or transmit your orders and may delay receipt of your order by the brokerage firm. Check with your particular brokerage firm on its notification procedures. And note that notification that the order was received does not mean that the order was executed.

Is my order executed immediately?

Orders entered electronically are usually executed quickly; however, there is no assurance that this will always occur. Investors should be aware that high trading volumes can cause delays in executions. Market volatility and delays in executions due to trading volume can result in trade executions at prices significantly different from the quoted price of the security at the time the order was entered. Also, different firms offer different levels of access and system sophistication. The speed of the Internet Service Provider used by an investor may also have an effect on order transmittal and execution. Timing in execution of orders may also be impacted by market volume, order queues at market centers, possible delays in order transmissions by brokers, and other systems issues.

What do the online brokerage rankings mean? If I open an account at a brokerage firm ranked #1, do I have a better chance of making money?

Generally, these rankings indicate the level of customer service or satisfaction with the online brokerage. There are many groups that provide 'ranking' services, and investors should keep in mind that these are not regulated entities. Further, different ranking groups use varying criteria and update their data on different schedules. You do not have a better chance of making money at a firm ranked #1 because the rankings do not relate to the likelihood of investment success.

What are the risks of online trading?

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Krishna Face
Publications

Accepted

VaPar Synth - A Variational Parametric Model for Audio Synthesis
Krishna Subramani, Preeti Rao Alexandre D'Hooge
ICASSP 2020
Paper / Code / Audio Examples / DOI / BibTeX

We present VaPar Synth - a Variational Parametric Synthesizer for instrument note synthesis, which utilizes a conditional variational autoencoder trained on a source-filter inspired parametric representation.

We will be presenting our work virtually at ICASSP 2020! (Presentation Video)

Energy-Weighted Multi-Band Novelty Functions for Onset Detection in Piano Music
Krishna Subramani, Srivatsan Sridhar, Rohit M A, Preeti Rao
National Conference on Communications 2018
Paper / DOI / BibTeX

Propose the use of energy-based weighting of multi-band onset detection functions and the use of a new criterion for adapting the final peak-picking threshold to improve detection of soft onsets in the vicinity of loud notes. Also propose a grouping algorithm to reduce the detection of spurious onsets.

We presented our work at the conference as an oral presentation

Generative Audio Synthesis with a Parametric Model
Krishna Subramani, Alexandre D'Hooge, Preeti Rao
ISMIR 2019 Late Breaking/Demo
Abstract / Audio Examples / BibTeX

Propose a parametric representation for audio corresponding more directly to its musical attributes such as pitch, dynamics and timbre. For more control over generation, we also propose the use of a conditional variational autoencoder which conditions the timbre on pitch.

We presented our work as a poster in the ISMIR 2019 Late Breaking/Demo session

Submitted

Learning Complex Representations from Spatial Phase Statistics of Natural Scenes
HaDi Maboudi, Krishna Subramani, Hamid Soltanian-Zadeh, Shun-ichi Amari, Hideaki Shimazaki
Submitted to Neural Networks, Under Review
Preprint / bioRxiv / BibTeX

We introduce a generative model for phase in visual systems, and propose a complex domain based maximum likelihood estimation procedure for parameter estimation. We derive analytical gradient expressions for maximum likelihood estimation using Wirtinger Calculus (detailed in our supplementary material)

I presented the initial part of this work as an oral presentation at Honda Research Institute

Research Experience
Variational Parametric Models for Audio Synthesis
Report / Repository / Review of Generative Models for Audio Synthesis

(Ongoing)My Master's thesis work on generative parametric models for audio synthesis.

I presented the work done by me so far as an oral presentation for the first stage defense of my Master's thesis

Compression using Graph Signal Processing
Presentation / Report / Notebook Demonstration of the Code

Inspired by the authors work on compressing distributed data by modeling it as graphs, we decided to try out and extend the idea to a new dataset by performing outlier removal, data imputation and clipping, and were able to successfully compress it at a significantly faster rate(~8x speedup) than conventional techniques.

https://www.sfu.ca/~truax/gran.html
Data Sonification using Granular Synthesis
Report / Code Repository

Implemented a real-time granular synthesizer on Pure Data. Also ideated a scheme where the parameters of the synth could be controlled externally with data, thus 'sonifying' the data!

Kuramoto Model and Oscillatory Networks
Image Recall

Implemented an image retrieval(memory recall) task using the Kuramoto Model. Extended this to try out graph coloring using Oscillatory Networks.


The Master Yoda to us Padawans.

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