http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-optional-2017&feed=atom&action=historyDocument:MIT-MFin-optional-2017 - Revision history2024-03-29T08:54:50ZRevision history for this page on the wikiMediaWiki 1.22.1http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-optional-2017&diff=1100&oldid=prevChangtau2005 at 10:36, 5 January 20172017-01-05T10:36:52Z<p></p>
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<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Since most of my professional and academic experience was in technology, deferring recruitment to the returning fall term allows me to concentrate academically throughout the first <del class="diffchange diffchange-inline">academic </del>year, to make my case more competitive. Most importantly, the three months of internship would have very high impact in terms of working experience in finance.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Since most of my professional and academic experience was in technology, deferring recruitment to the returning fall term allows me to concentrate academically throughout the first year, to make my case more competitive. Most importantly, the three months of internship would have very high impact in terms of working experience in finance.</div></td></tr>
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</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-optional-2017&diff=1095&oldid=prevChangtau2005 at 08:58, 5 January 20172017-01-05T08:58:06Z<p></p>
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<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>I am applying to the 18 month <del class="diffchange diffchange-inline">pilot </del>for two reasons:</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>I am applying to the 18 month <ins class="diffchange diffchange-inline">programme </ins>for two reasons:</div></td></tr>
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</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-optional-2017&diff=1094&oldid=prevChangtau2005 at 08:57, 5 January 20172017-01-05T08:57:54Z<p></p>
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<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The 18-format gives me more time to either take the thesis as far as I can, or to take additional units to inform it.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The 18-<ins class="diffchange diffchange-inline">month </ins>format gives me more time to either take the thesis as far as I can, or to take additional units to inform it.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td></tr>
</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-optional-2017&diff=1093&oldid=prevChangtau2005 at 08:56, 5 January 20172017-01-05T08:56:58Z<p></p>
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<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The 18-format <del class="diffchange diffchange-inline">allows </del>more time to <del class="diffchange diffchange-inline">work towards </del>the thesis, or to take additional units to inform it.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The 18-format <ins class="diffchange diffchange-inline">gives me </ins>more time to <ins class="diffchange diffchange-inline">either take </ins>the thesis <ins class="diffchange diffchange-inline">as far as I can</ins>, or to take additional units to inform it.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td></tr>
</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-optional-2017&diff=1092&oldid=prevChangtau2005 at 08:54, 5 January 20172017-01-05T08:54:31Z<p></p>
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<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The 18-format allows more time to work towards the thesis <del class="diffchange diffchange-inline">and </del>to take additional units to inform it.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The 18-format allows more time to work towards the thesis<ins class="diffchange diffchange-inline">, or </ins>to take additional units to inform it.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td></tr>
</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-optional-2017&diff=1091&oldid=prevChangtau2005 at 08:50, 5 January 20172017-01-05T08:50:38Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Such features from multiple time series could be used to, for example, forecast and refine the next-period expected return in portfolio management, or used in a manner that resembles pairs trading. Moreover, the representation lends itself to transfer learning; suppose we have insufficient information at long time-scales. We could train on higher-frequency data and either use the high-frequency features <i>a priori</i> or additionally refine <del class="diffchange diffchange-inline">it </del>with what we have. In this way, we potentially accrue an information advantage by learning common signals across multiple time scales, and the gradual transition between long and short term is modelled naturally.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Such features from multiple time series could be used to, for example, forecast and refine the next-period expected return in portfolio management, or used in a manner that resembles pairs trading. Moreover, the representation lends itself to transfer learning; suppose we have insufficient information at long time-scales. We could train on higher-frequency data and either use the high-frequency features <i>a priori</i> or additionally refine <ins class="diffchange diffchange-inline">them </ins>with what we have. In this way, we potentially accrue an information advantage by learning common signals across multiple time scales, and the gradual transition between long and short term is modelled naturally.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>}}</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>}}</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-optional-2017&diff=1090&oldid=prevChangtau2005 at 08:49, 5 January 20172017-01-05T08:49:24Z<p></p>
<table class='diff diff-contentalign-left'>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Such features from multiple time series could be used to, for example, forecast and refine the next-period expected return in portfolio management, or used in a manner that resembles pairs trading. Moreover, the representation lends itself to transfer learning; suppose we have insufficient information at long time-scales. We could train on higher-frequency data and either use the high-frequency features <i>a priori</i> or additionally refine it with what we have. In this way, we potentially accrue an information advantage by learning common signals across multiple time scales, and the gradual transition between long and short term is modelled naturally.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Such features from multiple time series could be used to, for example, forecast and refine the next-period expected return in portfolio management, or used in a manner that resembles pairs trading. Moreover, the representation lends itself to transfer learning; suppose we have insufficient information at long time-scales. We could train on higher-frequency data and either use the high-frequency features <i>a priori</i> or additionally refine it with what we have. In this way, we potentially accrue an information advantage by learning common signals across multiple time scales, and the gradual transition between long and short term is modelled naturally.</div></td></tr>
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</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-optional-2017&diff=1089&oldid=prevChangtau2005 at 08:46, 5 January 20172017-01-05T08:46:51Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Such features from multiple time series could be used to, for example, forecast and refine the next-period expected return in portfolio management, or used in a manner that resembles pairs trading.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Such features from multiple time series could be used to, for example, forecast and refine the next-period expected return in portfolio management, or used in a manner that resembles pairs trading. <ins class="diffchange diffchange-inline">Moreover, the </ins>representation lends itself to transfer learning; suppose we have insufficient information at long time-scales. We could train on higher-frequency data and either use the high-frequency features <i>a priori</i> or additionally refine it with what we have. In this way, we potentially accrue an information advantage by learning common signals across multiple time scales, and the gradual transition between long and short term is modelled naturally.</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div></div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">Such a </del>representation <del class="diffchange diffchange-inline">also </del>lends itself to transfer learning; suppose we have insufficient information at long time-scales. We could train on higher-frequency data and either use the high-frequency features <i>a priori</i> or additionally refine it with what we have. In this way, we potentially accrue an information advantage by learning common signals across multiple time scales, and the gradual transition between long and short term is modelled naturally.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div></div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-optional-2017&diff=1087&oldid=prevChangtau2005 at 08:44, 5 January 20172017-01-05T08:44:56Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{CVContent|</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{CVContent|</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The 18-format allows more time to <del class="diffchange diffchange-inline">both </del>work towards the thesis and to take additional units to inform it.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The 18-format allows more time to work towards the thesis and to take additional units to inform it.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td></tr>
</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-optional-2017&diff=1086&oldid=prevChangtau2005 at 08:44, 5 January 20172017-01-05T08:44:43Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{CVContent|</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{CVContent|</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The 18-format allows more time to work towards the thesis<del class="diffchange diffchange-inline">, or </del>to take additional units to inform it.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The 18-format allows more time to <ins class="diffchange diffchange-inline">both </ins>work towards the thesis <ins class="diffchange diffchange-inline">and </ins>to take additional units to inform it.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>My research is about the application of deep convolutional models to financial time series analysis. Such methods summarize time series (potentially with multiple channels, e.g. trade and market depth data) in terms of nonlinear functions of convolutions with filters learned automatically through optimization. The motivation is to capture relationships missed by hand-engineered or low-level features (e.g. technical indicators, RSI, correlation etc.) which are not necessarily optimal -- [Foundations of Technical Analysis. Lo 2000: section V]</div></td></tr>
</table>Changtau2005